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Sample records for automated clinical decision

  1. Automation in Clinical Microbiology

    Science.gov (United States)

    Ledeboer, Nathan A.

    2013-01-01

    Historically, the trend toward automation in clinical pathology laboratories has largely bypassed the clinical microbiology laboratory. In this article, we review the historical impediments to automation in the microbiology laboratory and offer insight into the reasons why we believe that we are on the cusp of a dramatic change that will sweep a wave of automation into clinical microbiology laboratories. We review the currently available specimen-processing instruments as well as the total laboratory automation solutions. Lastly, we outline the types of studies that will need to be performed to fully assess the benefits of automation in microbiology laboratories. PMID:23515547

  2. Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain

    Directory of Open Access Journals (Sweden)

    Clark Michael E

    2010-04-01

    Full Text Available Abstract Background Opioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients. Methods Here we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR, and a set of clinical tools. Results The iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools. Conclusions Use of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The

  3. Datafication of Automated (Legal) Decisions

    DEFF Research Database (Denmark)

    Schaumburg-Müller, Sten

    data machines may be able to (or are thought to be able to) make a prediction profile, leaving risks for individuals for being excluded from life and health insurances, being targets for computational policing etc. An additional dimension to the prefabricated decisions is the commercial aspect......) decisions which has implications for legal orders, legal actors and legal research, not to mention legal legitimacy as well as personal autonomy and democracy. On the one hand automation may facilitate better, faster, more predictable and more coherent decisions and leave cumbersome and time consuming...

  4. Implementation of a scalable, web-based, automated clinical decision support risk-prediction tool for chronic kidney disease using C-CDA and application programming interfaces.

    Science.gov (United States)

    Samal, Lipika; D'Amore, John D; Bates, David W; Wright, Adam

    2017-11-01

    Clinical decision support tools for risk prediction are readily available, but typically require workflow interruptions and manual data entry so are rarely used. Due to new data interoperability standards for electronic health records (EHRs), other options are available. As a clinical case study, we sought to build a scalable, web-based system that would automate calculation of kidney failure risk and display clinical decision support to users in primary care practices. We developed a single-page application, web server, database, and application programming interface to calculate and display kidney failure risk. Data were extracted from the EHR using the Consolidated Clinical Document Architecture interoperability standard for Continuity of Care Documents (CCDs). EHR users were presented with a noninterruptive alert on the patient's summary screen and a hyperlink to details and recommendations provided through a web application. Clinic schedules and CCDs were retrieved using existing application programming interfaces to the EHR, and we provided a clinical decision support hyperlink to the EHR as a service. We debugged a series of terminology and technical issues. The application was validated with data from 255 patients and subsequently deployed to 10 primary care clinics where, over the course of 1 year, 569 533 CCD documents were processed. We validated the use of interoperable documents and open-source components to develop a low-cost tool for automated clinical decision support. Since Consolidated Clinical Document Architecture-based data extraction extends to any certified EHR, this demonstrates a successful modular approach to clinical decision support. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  5. An Automated System for Generating Situation-Specific Decision Support in Clinical Order Entry from Local Empirical Data

    Science.gov (United States)

    Klann, Jeffrey G.

    2011-01-01

    Clinical Decision Support is one of the only aspects of health information technology that has demonstrated decreased costs and increased quality in healthcare delivery, yet it is extremely expensive and time-consuming to create, maintain, and localize. Consequently, a majority of health care systems do not utilize it, and even when it is…

  6. Decision Procedures for Automating Termination Proofs

    Science.gov (United States)

    Piskac, Ruzica; Wies, Thomas

    Automated termination provers often use the following schema to prove that a program terminates: construct a relational abstraction of the program's transition relation and then show that the relational abstraction is well-founded. The focus of current tools has been on developing sophisticated techniques for constructing the abstractions while relying on known decidable logics (such as linear arithmetic) to express them. We believe we can significantly increase the class of programs that are amenable to automated termination proofs by identifying more expressive decidable logics for reasoning about well-founded relations. We therefore present a new decision procedure for reasoning about multiset orderings, which are among the most powerful orderings used to prove termination. We show that, using our decision procedure, one can automatically prove termination of natural abstractions of programs.

  7. Decision analysis using decision trees for a simple clinical decision.

    Science.gov (United States)

    Blakley, Brian

    2012-10-01

    To illustrate the use of decision trees with a utility index in clinical decision making. A decision tree was created related to whether or not to perform a tonsillectomy. Data from the literature were applied to a common hypothetical clinical scenario. A decision tree graphically represents the typical decision-making process that many clinicians use. The addition of utility functions permitted consideration of the adverse or beneficial effects of outcomes, altering the treatment decision. Quantitative tools such as decision trees may quantify outcome preferences and aid in clinical decision making, but the proper tool and background data are essential.

  8. Shared clinical decision making

    Science.gov (United States)

    AlHaqwi, Ali I.; AlDrees, Turki M.; AlRumayyan, Ahmad; AlFarhan, Ali I.; Alotaibi, Sultan S.; AlKhashan, Hesham I.; Badri, Motasim

    2015-01-01

    Objectives: To determine preferences of patients regarding their involvement in the clinical decision making process and the related factors in Saudi Arabia. Methods: This cross-sectional study was conducted in a major family practice center in King Abdulaziz Medical City, Riyadh, Saudi Arabia, between March and May 2012. Multivariate multinomial regression models were fitted to identify factors associated with patients preferences. Results: The study included 236 participants. The most preferred decision-making style was shared decision-making (57%), followed by paternalistic (28%), and informed consumerism (14%). The preference for shared clinical decision making was significantly higher among male patients and those with higher level of education, whereas paternalism was significantly higher among older patients and those with chronic health conditions, and consumerism was significantly higher in younger age groups. In multivariate multinomial regression analysis, compared with the shared group, the consumerism group were more likely to be female [adjusted odds ratio (AOR) =2.87, 95% confidence interval [CI] 1.31-6.27, p=0.008] and non-dyslipidemic (AOR=2.90, 95% CI: 1.03-8.09, p=0.04), and the paternalism group were more likely to be older (AOR=1.03, 95% CI: 1.01-1.05, p=0.04), and female (AOR=2.47, 95% CI: 1.32-4.06, p=0.008). Conclusion: Preferences of patients for involvement in the clinical decision-making varied considerably. In our setting, underlying factors that influence these preferences identified in this study should be considered and tailored individually to achieve optimal treatment outcomes. PMID:26620990

  9. Automated Sleep Stage Scoring by Decision Tree Learning

    National Research Council Canada - National Science Library

    Hanaoka, Masaaki

    2001-01-01

    In this paper we describe a waveform recognition method that extracts characteristic parameters from wave- forms and a method of automated sleep stage scoring using decision tree learning that is in...

  10. An automated approach to the design of decision tree classifiers

    Science.gov (United States)

    Argentiero, P.; Chin, R.; Beaudet, P.

    1982-01-01

    An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.

  11. The Level of Automation in Emergency Quick Disconnect Decision Making

    Directory of Open Access Journals (Sweden)

    Imset Marius

    2018-02-01

    Full Text Available As a key measure for safety and environmental protection during offshore well operations, drill rigs are equipped with Emergency Quick Disconnect (EQD systems. However, an EQD operation is in itself considered a risky operation with a major economic impact. For this reason, it is of great importance to aid the operators in their assessment of the situation at all times, and help them make the best decisions. However, despite the availability of such systems, accidents do happen. This demonstrates the vulnerability of our human decision-making capabilities in extremely stressful situations. One way of improving the overall human-system performance with respect to EQD is to increase the level and quality of the automation and decision support systems. Although there is plenty of evidence that automated systems have weaknesses, there is also evidence that advanced software systems outperform humans in complex decision-making. The major challenge is to make sure that EQD is performed when necessary, but there is also a need to decrease the number of false EQDs. This paper applies an existing framework for levels of automation in order to explore the critical decision process leading to an EQD. We provide an overview of the benefits and drawbacks of existing automation and decision support systems vs. manual human decision-making. Data are collected from interviews of offshore users, suppliers, and oil companies, as well as from formal operational procedures. Findings are discussed using an established framework for the level of automation. Our conclusion is that there is an appropriate level of automation in critical situations related to the loss of the position of the drill rig, and that there is the promising potential to increase the autonomy level in a mid- and long-term situation assessment.

  12. Clinical Decision Support (CDS) Inventory

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Clinical Decision Support (CDS) Inventory contains descriptions of past and present CDS projects across the Federal Government. It includes Federal projects,...

  13. Automation in the clinical microbiology laboratory.

    Science.gov (United States)

    Novak, Susan M; Marlowe, Elizabeth M

    2013-09-01

    Imagine a clinical microbiology laboratory where a patient's specimens are placed on a conveyor belt and sent on an automation line for processing and plating. Technologists need only log onto a computer to visualize the images of a culture and send to a mass spectrometer for identification. Once a pathogen is identified, the system knows to send the colony for susceptibility testing. This is the future of the clinical microbiology laboratory. This article outlines the operational and staffing challenges facing clinical microbiology laboratories and the evolution of automation that is shaping the way laboratory medicine will be practiced in the future. Copyright © 2013 Elsevier Inc. All rights reserved.

  14. Automated radiochemical processing for clinical PET

    International Nuclear Information System (INIS)

    Padgett, H.C.; Kingsbury, W.G.

    1990-01-01

    The Siemens RDS 112, an automated radiochemical production and delivery system designed to support a clinical PET program, consists of an 11 MeV, proton only, negative ion cyclotron, a shield, a computer, and targetry and chemical processing modules to produce radiochemicals used in PET imaging. The principal clinical PET tracers are [ 18 F]FDG, [ 13 N]ammonia and [ 15 O]water. Automated synthesis of [ 18 F]FDG is achieved using the Chemistry Process Control Unit (CPCU), a general purpose valve-and-tubing device that emulates manual processes while allowing for competent operator intervention. Using function-based command file software, this pressure-driven synthesis system carries out chemical processing procedures by timing only, without process-based feedback. To date, nine CPCUs have installed at seven institutions resulting in 1,200+ syntheses of [ 18 F]FDG, with an average yield of 55% (EOB)

  15. Decision Making In A High-Tech World: Automation Bias and Countermeasures

    Science.gov (United States)

    Mosier, Kathleen L.; Skitka, Linda J.; Burdick, Mark R.; Heers, Susan T.; Rosekind, Mark R. (Technical Monitor)

    1996-01-01

    Automated decision aids and decision support systems have become essential tools in many high-tech environments. In aviation, for example, flight management systems computers not only fly the aircraft, but also calculate fuel efficient paths, detect and diagnose system malfunctions and abnormalities, and recommend or carry out decisions. Air Traffic Controllers will soon be utilizing decision support tools to help them predict and detect potential conflicts and to generate clearances. Other fields as disparate as nuclear power plants and medical diagnostics are similarly becoming more and more automated. Ideally, the combination of human decision maker and automated decision aid should result in a high-performing team, maximizing the advantages of additional cognitive and observational power in the decision-making process. In reality, however, the presence of these aids often short-circuits the way that even very experienced decision makers have traditionally handled tasks and made decisions, and introduces opportunities for new decision heuristics and biases. Results of recent research investigating the use of automated aids have indicated the presence of automation bias, that is, errors made when decision makers rely on automated cues as a heuristic replacement for vigilant information seeking and processing. Automation commission errors, i.e., errors made when decision makers inappropriately follow an automated directive, or automation omission errors, i.e., errors made when humans fail to take action or notice a problem because an automated aid fails to inform them, can result from this tendency. Evidence of the tendency to make automation-related omission and commission errors has been found in pilot self reports, in studies using pilots in flight simulations, and in non-flight decision making contexts with student samples. Considerable research has found that increasing social accountability can successfully ameliorate a broad array of cognitive biases and

  16. Adaptive Automation Based on Air Traffic Controller Decision-Making

    NARCIS (Netherlands)

    IJtsma (Student TU Delft), Martijn; Borst, C.; Mercado Velasco, G.A.; Mulder, M.; van Paassen, M.M.; Tsang, P.S.; Vidulich, M.A.

    2017-01-01

    Through smart scheduling and triggering of automation support, adaptive automation has the potential to balance air traffic controller workload. The challenge in the design of adaptive automation systems is to decide how and when the automation should provide support. This paper describes the design

  17. Decision-making and problem solving methods in automation technology

    Energy Technology Data Exchange (ETDEWEB)

    Hankins, W.W.; Pennington, J.E.; Barker, L.K.

    1983-05-01

    This report presents a brief review of the state of the art in the automation of decision making and problem solving. The information upon which the report is based was derived from literature searches, visits to university and government laboratories performing basic research in the area, and a 1980 Langley Research Center sponsored conference on the subject. It is the contention of the authors that the technology in this area is being generated by research primarily in the three disciplines of Artificial Intelligence, Control Theory, and Operations Research. Under the assumption that the state of the art in decision making and problem solving is reflected in the problems being solved, specific problems and methods of their solution are often discussed to elucidate particular aspects of the subject. Synopses of the following major topic areas comprise most of the report: (1) detection and recognition; (2) planning and scheduling; (3) learning; (4) theorem proving; (5) distributed systems; (6) knowledge bases; (7) search; (8) heuristics; and (9) evolutionary programming.

  18. Patients' Values in Clinical Decision-Making.

    Science.gov (United States)

    Faggion, Clovis Mariano; Pachur, Thorsten; Giannakopoulos, Nikolaos Nikitas

    2017-09-01

    Shared decision-making involves the participation of patient and dental practitioner. Well-informed decision-making requires that both parties understand important concepts that may influence the decision. This fourth article in a series of 4 aims to discuss the importance of patients' values when a clinical decision is made. We report on how to incorporate important concepts for well-informed, shared decision-making. Here, we present patient values as an important issue, in addition to previously established topics such as the risk of bias of a study, cost-effectiveness of treatment approaches, and a comparison of therapeutic benefit with potential side effects. We provide 2 clinical examples and suggestions for a decision tree, based on the available evidence. The information reported in this article may improve the relationship between patient and dental practitioner, resulting in more well-informed clinical decisions. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Laboratory automation in clinical bacteriology: what system to choose?

    Science.gov (United States)

    Croxatto, A; Prod'hom, G; Faverjon, F; Rochais, Y; Greub, G

    2016-03-01

    Automation was introduced many years ago in several diagnostic disciplines such as chemistry, haematology and molecular biology. The first laboratory automation system for clinical bacteriology was released in 2006, and it rapidly proved its value by increasing productivity, allowing a continuous increase in sample volumes despite limited budgets and personnel shortages. Today, two major manufacturers, BD Kiestra and Copan, are commercializing partial or complete laboratory automation systems for bacteriology. The laboratory automation systems are rapidly evolving to provide improved hardware and software solutions to optimize laboratory efficiency. However, the complex parameters of the laboratory and automation systems must be considered to determine the best system for each given laboratory. We address several topics on laboratory automation that may help clinical bacteriologists to understand the particularities and operative modalities of the different systems. We present (a) a comparison of the engineering and technical features of the various elements composing the two different automated systems currently available, (b) the system workflows of partial and complete laboratory automation, which define the basis for laboratory reorganization required to optimize system efficiency, (c) the concept of digital imaging and telebacteriology, (d) the connectivity of laboratory automation to the laboratory information system, (e) the general advantages and disadvantages as well as the expected impacts provided by laboratory automation and (f) the laboratory data required to conduct a workflow assessment to determine the best configuration of an automated system for the laboratory activities and specificities. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  20. Personalized Clinical Decision Making in Gastrointestinal Malignancies

    DEFF Research Database (Denmark)

    Hess, Søren; Bjerring, Ole Steen; Pfeiffer, Per

    2016-01-01

    and initial stages. This article outlines the potential use of fluorodeoxyglucose-PET/CT in clinical decision making with special regard to preoperative evaluation and response assessment in gastric cancer (including the gastroesophageal junction), pancreatic cancer (excluding neuroendocrine tumors...

  1. Clinical evaluation of an automated turning bed.

    Science.gov (United States)

    Melland, H I; Langemo, D; Hanson, D; Olson, B; Hunter, S

    1999-01-01

    The purposes of this study were to assess client comfort and sleep quality, client physiologic response (skin and respiratory status), the effect on the need for caregiver assistance, and cost when using an automated turning bed. Nonexperimental, evaluative study. Twenty-four adult home or long-term care resident subjects who had a degenerative disease, spinal cord injury, stroke, cerebral palsy, or back surgery. Each subject agreed to use the automated turning bed for four weeks. Researchers completed a demographic survey and skin assessment, and assessed each subject for pressure ulcer risk and for the need of assistance of a care giver for turning before and after the four weeks of using the turning bed. Subjects rated the turning bed in terms of comfort and sleep quality. Subjects rated the turning bed as more comfortable than their own bed and expressed satisfaction at the pain relief attained when on the turning bed. While using the turning bed, there was a significant improvement in sleep quality. No skin breakdown or deterioration in respiratory status occurred. Fewer subjects required the assistance of a caregiver for turning when on the turning bed. This automated turning bed shows great promise in meeting a need for patients with limited mobility whether they are homebound or in a residential community. Future studies that further investigate use of the turning bed for postoperative back patients while still in the acute care setting are indicated. Replicative studies with a larger sample size are also indicated.

  2. Assay-specific decision limits for two new automated parathyroid hormone and 25-hydroxyvitamin D assays.

    Science.gov (United States)

    Souberbielle, Jean-Claude; Fayol, Véronique; Sault, Corinne; Lawson-Body, Ethel; Kahan, André; Cormier, Catherine

    2005-02-01

    The recent development of nonradioactive automated assays for serum parathyroid hormone (PTH) and 25-hydroxyvitamin D (25OHD) has made measurement of these two hormones possible in many laboratories. In this study, we compared two new assays for PTH and 25OHD adapted on an automated analyzer, the LIAISON, with two manual immunoassays used worldwide. We studied 228 osteoporotic patients, 927 healthy individuals, 38 patients with primary hyperparathyroidism, and 167 hemodialyzed patients. Serum PTH was measured with the Allegro and the LIAISON assays, and 25OHD was measured with DiaSorin RIA and the LIAISON assay. Regression analysis was used to calculate decision thresholds for the LIAISON assays that were equivalent to those of the Allegro PTH and DiaSorin 25OHD assays. The 25OHD concentrations obtained with the LIAISON assay and the RIA in osteoporotic patients were well correlated (r = 0.83; P 50 nmol/L as eligible for the reference population for the LIAISON PTH assay. In this group, the 3rd-97th percentile interval for LIAISON PTH was 3-51 ng/L. Considering upper reference limits of 46 and 51 ng/L for the Allegro and LIAISON assays, respectively, the frequency of above-normal PTH concentrations in patients with primary hyperparathyroidism was similar in both assays. Regression analysis between serum PTH measured by the Allegro and LIAISON assays in 167 hemodialyzed patients and the corresponding Bland-Altman analysis of these data suggest that the LIAISON PTH assay tends to read higher than the Allegro assay at low concentrations but lower at high concentrations (>300 ng/L). Because clinical decision limits for both PTH and 25OHD should be assay specific, we propose equivalences between these assays and two manual assays used worldwide. These assay-specific decision limits should help potential users of the LIAISON PTH and 25OHD assays.

  3. Clinical decision support system in dental implantology

    OpenAIRE

    Alexandra Polášková; Jitka Feberová; Taťjána Dostálová; Pavel Kříž; Michaela Seydlová

    2013-01-01

    Implantology is rapidly developing interdisciplinary field providing enormous amounts of data to be classified, evaluated and interpreted. The analysis of clinical data remains a big challenge, because each new system has specific requirements. The aim of study was prepare specific tool for treatment planning. Decision support system is built on Expert system. It is interactive software which provides clinical recommendations and treatment planning. Expert systems are knowledge-based computer...

  4. Automation and Accountability in Decision Support System Interface Design

    Science.gov (United States)

    Cummings, Mary L.

    2006-01-01

    When the human element is introduced into decision support system design, entirely new layers of social and ethical issues emerge but are not always recognized as such. This paper discusses those ethical and social impact issues specific to decision support systems and highlights areas that interface designers should consider during design with an…

  5. From Trust in Automation to Decision Neuroscience: Applying Cognitive Neuroscience Methods to Understand and Improve Interaction Decisions Involved in Human Automation Interaction.

    Science.gov (United States)

    Drnec, Kim; Marathe, Amar R; Lukos, Jamie R; Metcalfe, Jason S

    2016-01-01

    Human automation interaction (HAI) systems have thus far failed to live up to expectations mainly because human users do not always interact with the automation appropriately. Trust in automation (TiA) has been considered a central influence on the way a human user interacts with an automation; if TiA is too high there will be overuse, if TiA is too low there will be disuse. However, even though extensive research into TiA has identified specific HAI behaviors, or trust outcomes, a unique mapping between trust states and trust outcomes has yet to be clearly identified. Interaction behaviors have been intensely studied in the domain of HAI and TiA and this has led to a reframing of the issues of problems with HAI in terms of reliance and compliance. We find the behaviorally defined terms reliance and compliance to be useful in their functionality for application in real-world situations. However, we note that once an inappropriate interaction behavior has occurred it is too late to mitigate it. We therefore take a step back and look at the interaction decision that precedes the behavior. We note that the decision neuroscience community has revealed that decisions are fairly stereotyped processes accompanied by measurable psychophysiological correlates. Two literatures were therefore reviewed. TiA literature was extensively reviewed in order to understand the relationship between TiA and trust outcomes, as well as to identify gaps in current knowledge. We note that an interaction decision precedes an interaction behavior and believe that we can leverage knowledge of the psychophysiological correlates of decisions to improve joint system performance. As we believe that understanding the interaction decision will be critical to the eventual mitigation of inappropriate interaction behavior, we reviewed the decision making literature and provide a synopsis of the state of the art understanding of the decision process from a decision neuroscience perspective. We forward

  6. From Trust in Automation to Decision Neuroscience: Applying Cognitive Neuroscience Methods to Understand and Improve Interaction Decisions Involved in Human Automation Interaction

    Science.gov (United States)

    Drnec, Kim; Marathe, Amar R.; Lukos, Jamie R.; Metcalfe, Jason S.

    2016-01-01

    Human automation interaction (HAI) systems have thus far failed to live up to expectations mainly because human users do not always interact with the automation appropriately. Trust in automation (TiA) has been considered a central influence on the way a human user interacts with an automation; if TiA is too high there will be overuse, if TiA is too low there will be disuse. However, even though extensive research into TiA has identified specific HAI behaviors, or trust outcomes, a unique mapping between trust states and trust outcomes has yet to be clearly identified. Interaction behaviors have been intensely studied in the domain of HAI and TiA and this has led to a reframing of the issues of problems with HAI in terms of reliance and compliance. We find the behaviorally defined terms reliance and compliance to be useful in their functionality for application in real-world situations. However, we note that once an inappropriate interaction behavior has occurred it is too late to mitigate it. We therefore take a step back and look at the interaction decision that precedes the behavior. We note that the decision neuroscience community has revealed that decisions are fairly stereotyped processes accompanied by measurable psychophysiological correlates. Two literatures were therefore reviewed. TiA literature was extensively reviewed in order to understand the relationship between TiA and trust outcomes, as well as to identify gaps in current knowledge. We note that an interaction decision precedes an interaction behavior and believe that we can leverage knowledge of the psychophysiological correlates of decisions to improve joint system performance. As we believe that understanding the interaction decision will be critical to the eventual mitigation of inappropriate interaction behavior, we reviewed the decision making literature and provide a synopsis of the state of the art understanding of the decision process from a decision neuroscience perspective. We forward

  7. Optimizing Decision Preparedness by Adapting Scenario Complexity and Automating Scenario Generation

    Science.gov (United States)

    Dunne, Rob; Schatz, Sae; Flore, Stephen M.; Nicholson, Denise

    2011-01-01

    Klein's recognition-primed decision (RPD) framework proposes that experts make decisions by recognizing similarities between current decision situations and previous decision experiences. Unfortunately, military personnel arQ often presented with situations that they have not experienced before. Scenario-based training (S8T) can help mitigate this gap. However, SBT remains a challenging and inefficient training approach. To address these limitations, the authors present an innovative formulation of scenario complexity that contributes to the larger research goal of developing an automated scenario generation system. This system will enable trainees to effectively advance through a variety of increasingly complex decision situations and experiences. By adapting scenario complexities and automating generation, trainees will be provided with a greater variety of appropriately calibrated training events, thus broadening their repositories of experience. Preliminary results from empirical testing (N=24) of the proof-of-concept formula are presented, and future avenues of scenario complexity research are also discussed.

  8. IBM's Health Analytics and Clinical Decision Support.

    Science.gov (United States)

    Kohn, M S; Sun, J; Knoop, S; Shabo, A; Carmeli, B; Sow, D; Syed-Mahmood, T; Rapp, W

    2014-08-15

    This survey explores the role of big data and health analytics developed by IBM in supporting the transformation of healthcare by augmenting evidence-based decision-making. Some problems in healthcare and strategies for change are described. It is argued that change requires better decisions, which, in turn, require better use of the many kinds of healthcare information. Analytic resources that address each of the information challenges are described. Examples of the role of each of the resources are given. There are powerful analytic tools that utilize the various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions. There are early suggestions that these resources have clinical value. As with all analytic tools, they are limited by the amount and quality of data. Big data is an inevitable part of the future of healthcare. There is a compelling need to manage and use big data to make better decisions to support the transformation of healthcare to the personalized, evidence-supported model of the future. Cognitive computing resources are necessary to manage the challenges in employing big data in healthcare. Such tools have been and are being developed. The analytic resources, themselves, do not drive, but support healthcare transformation.

  9. Clinical decision support system in dental implantology

    Directory of Open Access Journals (Sweden)

    Alexandra Polášková

    2013-06-01

    Full Text Available Implantology is rapidly developing interdisciplinary field providing enormous amounts of data to be classified, evaluated and interpreted. The analysis of clinical data remains a big challenge, because each new system has specific requirements. The aim of study was prepare specific tool for treatment planning. Decision support system is built on Expert system. It is interactive software which provides clinical recommendations and treatment planning. Expert systems are knowledge-based computer programs designed to provide assistance in diagnosis and treatment planning. These systems are used for health care (dentistry, medicine, pharmacy etc.. The application contained the medical history analysis to obtaining information useful in formulating a diagnosis and providing implant insertion and prosthetic reconstruction to the patient; the diagnostic examination of dental implant procedure; implant positioning diagnosis – 3-D measurement; diagnostic information for treatment planning; treatment plan in the form of objective measurement of implant placement that helps surgeon and prosthodontics. The decision algorithm implemented by programming language is used. Core of program is an expert knowledge programming like a decision tree. The analysis of the decision-making process for implant treatment in general practice is prepared and analyzed.

  10. Dialogic Consensus In Clinical Decision-Making.

    Science.gov (United States)

    Walker, Paul; Lovat, Terry

    2016-12-01

    This paper is predicated on the understanding that clinical encounters between clinicians and patients should be seen primarily as inter-relations among persons and, as such, are necessarily moral encounters. It aims to relocate the discussion to be had in challenging medical decision-making situations, including, for example, as the end of life comes into view, onto a more robust moral philosophical footing than is currently commonplace. In our contemporary era, those making moral decisions must be cognizant of the existence of perspectives other than their own, and be attuned to the demands of inter-subjectivity. Applicable to clinical practice, we propose and justify a Habermasian approach as one useful means of achieving what can be described as dialogic consensus. The Habermasian approach builds around, first, his discourse theory of morality as universalizable to all and, second, communicative action as a cooperative search for truth. It is a concrete way to ground the discourse which must be held in complex medical decision-making situations, in its actual reality. Considerations about the theoretical underpinnings of the application of dialogic consensus to clinical practice, and potential difficulties, are explored.

  11. BUBBLES: an Automated Decision Support System for Final Approach Controllers

    Science.gov (United States)

    Chi, Zhizang

    1990-01-01

    With the assumptions that an explicit schedule exists for landings (and takeoffs) at each runway, that each aircraft has declared an IAS for final approach and will be obligated to fly it as accurately as possible, and that there is a continuous estimate of average windspeed on approach, the objective was to provide automated cues to assist controllers in the spacing of landing aircraft. The cues have two characteristics. First, they are adaptive to estimation errors in position and speed by the radar tracking process and piloting errors in the execution of turns and commanded speed reductions. Second, the cues are responsive to the desires of the human controller. Several diagrams are used to help explain the system.

  12. A distributed clinical decision support system architecture

    Directory of Open Access Journals (Sweden)

    Shaker H. El-Sappagh

    2014-01-01

    Full Text Available This paper proposes an open and distributed clinical decision support system architecture. This technical architecture takes advantage of Electronic Health Record (EHR, data mining techniques, clinical databases, domain expert knowledge bases, available technologies and standards to provide decision-making support for healthcare professionals. The architecture will work extremely well in distributed EHR environments in which each hospital has its own local EHR, and it satisfies the compatibility, interoperability and scalability objectives of an EHR. The system will also have a set of distributed knowledge bases. Each knowledge base will be specialized in a specific domain (i.e., heart disease, and the model achieves cooperation, integration and interoperability between these knowledge bases. Moreover, the model ensures that all knowledge bases are up-to-date by connecting data mining engines to each local knowledge base. These data mining engines continuously mine EHR databases to extract the most recent knowledge, to standardize it and to add it to the knowledge bases. This framework is expected to improve the quality of healthcare, reducing medical errors and guaranteeing the safety of patients by helping clinicians to make correct, accurate, knowledgeable and timely decisions.

  13. Decision-theoretic refinement planning: a new method for clinical decision analysis.

    OpenAIRE

    Doan, A.; Haddawy, P.; Kahn, C. E.

    1995-01-01

    Clinical decision analysis seeks to identify the optimal management strategy by modelling the uncertainty and risks entailed in the diagnosis, natural history, and treatment of a particular problem or disorder. Decision trees are the most frequently used model in clinical decision analysis, but can be tedious to construct, cumbersome to use, and computationally prohibitive, especially with large, complex decision problems. We present a new method for clinical decision analysis that combines t...

  14. Towards automated calculation of evidence-based clinical scores.

    Science.gov (United States)

    Aakre, Christopher A; Dziadzko, Mikhail A; Herasevich, Vitaly

    2017-03-26

    To determine clinical scores important for automated calculation in the inpatient setting. A modified Delphi methodology was used to create consensus of important clinical scores for inpatient practice. A list of 176 externally validated clinical scores were identified from freely available internet-based services frequently used by clinicians. Scores were categorized based on pertinent specialty and a customized survey was created for each clinician specialty group. Clinicians were asked to rank each score based on importance of automated calculation to their clinical practice in three categories - "not important", "nice to have", or "very important". Surveys were solicited via specialty-group listserv over a 3-mo interval. Respondents must have been practicing physicians with more than 20% clinical time spent in the inpatient setting. Within each specialty, consensus was established for any clinical score with greater than 70% of responses in a single category and a minimum of 10 responses. Logistic regression was performed to determine predictors of automation importance. Seventy-nine divided by one hundred and forty-four (54.9%) surveys were completed and 72/144 (50%) surveys were completed by eligible respondents. Only the critical care and internal medicine specialties surpassed the 10-respondent threshold (14 respondents each). For internists, 2/110 (1.8%) of scores were "very important" and 73/110 (66.4%) were "nice to have". For intensivists, no scores were "very important" and 26/76 (34.2%) were "nice to have". Only the number of medical history (OR = 2.34; 95%CI: 1.26-4.67; P calculation. Future efforts towards score calculator automation should focus on technically feasible "nice to have" scores.

  15. Pilot interaction with automated airborne decision making systems

    Science.gov (United States)

    Hammer, John M.; Wan, C. Yoon; Vasandani, Vijay

    1987-01-01

    The current research is focused on detection of human error and protection from its consequences. A program for monitoring pilot error by comparing pilot actions to a script was described. It dealt primarily with routine errors (slips) that occurred during checklist activity. The model to which operator actions were compared was a script. Current research is an extension along these two dimensions. The ORS fault detection aid uses a sophisticated device model rather than a script. The newer initiative, the model-based and constraint-based warning system, uses an even more sophisticated device model and is to prevent all types of error, not just slips or bad decision.

  16. Decision support system for outage management and automated crew dispatch

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Ning; Mousavi, Mirrasoul

    2018-01-23

    A decision support system is provided for utility operations to assist with crew dispatch and restoration activities following the occurrence of a disturbance in a multiphase power distribution network, by providing a real-time visualization of possible location(s). The system covers faults that occur on fuse-protected laterals. The system uses real-time data from intelligent electronics devices coupled with other data sources such as static feeder maps to provide a complete picture of the disturbance event, guiding the utility crew to the most probable location(s). This information is provided in real-time, reducing restoration time and avoiding more costly and laborious fault location finding practices.

  17. Automated Decision-Making and Big Data: Concerns for People With Mental Illness.

    Science.gov (United States)

    Monteith, Scott; Glenn, Tasha

    2016-12-01

    Automated decision-making by computer algorithms based on data from our behaviors is fundamental to the digital economy. Automated decisions impact everyone, occurring routinely in education, employment, health care, credit, and government services. Technologies that generate tracking data, including smartphones, credit cards, websites, social media, and sensors, offer unprecedented benefits. However, people are vulnerable to errors and biases in the underlying data and algorithms, especially those with mental illness. Algorithms based on big data from seemingly unrelated sources may create obstacles to community integration. Voluntary online self-disclosure and constant tracking blur traditional concepts of public versus private data, medical versus non-medical data, and human versus automated decision-making. In contrast to sharing sensitive information with a physician in a confidential relationship, there may be numerous readers of information revealed online; data may be sold repeatedly; used in proprietary algorithms; and are effectively permanent. Technological changes challenge traditional norms affecting privacy and decision-making, and continued discussions on new approaches to provide privacy protections are needed.

  18. Development of an Automated Decision-Making Tool for Supervisory Control System

    Energy Technology Data Exchange (ETDEWEB)

    Cetiner, Sacit M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Muhlheim, Michael David [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Flanagan, George F. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Fugate, David L. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Kisner, Roger A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2014-09-01

    This technical report was generated as a product of the Supervisory Control for Multi-Modular Small Modular Reactor (SMR) Plants project within the Instrumentation, Control and Human-Machine Interface technology area under the Advanced Small Modular Reactor (AdvSMR) Research and Development Program of the US Department of Energy. The report documents the definition of strategies, functional elements, and the structural architecture of a supervisory control system for multi-modular AdvSMR plants. This research activity advances the state of the art by incorporating real-time, probabilistic-based decision-making into the supervisory control system architectural layers through the introduction of a tiered-plant system approach. The report provides background information on the state of the art of automated decision-making, including the description of existing methodologies. It then presents a description of a generalized decision-making framework, upon which the supervisory control decision-making algorithm is based. The probabilistic portion of automated decision-making is demonstrated through a simple hydraulic loop example.

  19. Segmentation of hip cartilage in compositional magnetic resonance imaging: A fast, accurate, reproducible, and clinically viable semi-automated methodology.

    Science.gov (United States)

    Fernquest, Scott; Park, Daniel; Marcan, Marija; Palmer, Antony; Voiculescu, Irina; Glyn-Jones, Sion

    2018-02-22

    Manual segmentation is a significant obstacle in the analysis of compositional MRI for clinical decision-making and research. Our aim was to produce a fast, accurate, reproducible, and clinically viable semi-automated method for segmentation of hip MRI. We produced a semi-automated segmentation method for cartilage segmentation of hip MRI sequences consisting of a two step process: (i) fully automated hierarchical partitioning of the data volume generated using a bespoke segmentation approach applied recursively, followed by (ii) user selection of the regions of interest using a region editor. This was applied to dGEMRIC scans at 3T taken from a prospective longitudinal study of individuals considered at high-risk of developing osteoarthritis (SibKids) which were also manually segmented for comparison. Fourteen hips were segmented both manually and using our semi-automated method. Per hip, processing time for semi-automated and manual segmentation was 10-15, and 60-120 min, respectively. Accuracy and Dice similarity coefficient (DSC) for the comparison of semi-automated and manual segmentations was 0.9886 and 0.8803, respectively. Intra-observer and inter-observer reproducibility of the semi-automated segmentation method gave an accuracy of 0.9997 and 0.9991, and DSC of 0.9726 and 0.9354, respectively. We have proposed a fast, accurate, reproducible, and clinically viable semi-automated method for segmentation of hip MRI sequences. This enables accurate anatomical and biochemical measurements to be obtained quickly and reproducibly. This is the first such method that shows clinical applicability, and could have large ramifications for the use of compositional MRI in research and clinically. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

  20. Clinical Decision Support Tools: The Evolution of a Revolution

    NARCIS (Netherlands)

    Mould, D. R.; D'Haens, G.; Upton, R. N.

    2016-01-01

    Dashboard systems for clinical decision support integrate data from multiple sources. These systems, the newest in a long line of dose calculators and other decision support tools, utilize Bayesian approaches to fully individualize dosing using information gathered through therapeutic drug

  1. The automation of clinical trial serious adverse event reporting workflow.

    Science.gov (United States)

    London, Jack W; Smalley, Karl J; Conner, Kyle; Smith, J Bruce

    2009-10-01

    The reporting of serious adverse events is a requirement when conducting a clinical trial involving human subjects, necessary for the protection of the participants. The reporting process is a multi-step procedure, involving a number of individuals from initiation to final review, and must be completed in a timely fashion. The purpose of this project was to automate the adverse event reporting process, replacing paper-based processes with computer-based processes, so that personnel effort and time required for serious adverse event reporting was reduced, and the monitoring of reporting performance and adverse event characteristics was facilitated. Use case analysis was employed to understand the reporting workflow and generate software requirements. The automation of the workflow was then implemented, employing computer databases, web-based forms, electronic signatures, and email communication. In the initial year (2007) of full deployment, 588 SAE reports were processed by the automated system, eSAEy. The median time from initiation to Principal Investigator electronic signature was trials adverse reporting procedures are applicable in general, specific workflow details may not be relevant at other institutions. The system facilitated analysis of individual investigator reporting performance, as well as the aggregation and analysis of the nature of reported adverse events.

  2. Automation of steroid radioimmunoassays for clinical and research purposes

    International Nuclear Information System (INIS)

    Vihko, R.; Hammond, G.L.

    1979-01-01

    In recent years an exponential increase in the research and clinical application of radioimmunoassays has created a demand for manipulative aids, to increase the general efficiency of the technique as well as precision of the determinations. At present a number of systems exist for sample preparation and the automation of assays. However, due to inherent problems of carry-over, priming, cleaning and the necessity for chemically inert components most of these systems tend to be rather inflexible. The instrument of choice ought therefore to be extremely versatile and provide the operator with a wide spectrum of alternatives, in order to optimize the initial capital outlay. Moreover, in the light of technological developments in the field of simultaneous, multisample gamma-counting and data processing, it is anticipated that a new generation of multisample processors will emerge of sufficient flexibility to accommodate the wide variety of assay protocols in present use. In this paper the problems encountered in the development of automated techniques for the radioimmunoassay of steroid hormones are reviewed, and a preliminary description of a versatile modular discrete instrument for the automation of radioimmunoassays is presented, which is based on simultaneous multisample preparation, and subsequent counting and data processing. (author)

  3. Intelligent Case Based Decision Support System for Online Diagnosis of Automated Production System

    Science.gov (United States)

    Ben Rabah, N.; Saddem, R.; Ben Hmida, F.; Carre-Menetrier, V.; Tagina, M.

    2017-01-01

    Diagnosis of Automated Production System (APS) is a decision-making process designed to detect, locate and identify a particular failure caused by the control law. In the literature, there are three major types of reasoning for industrial diagnosis: the first is model-based, the second is rule-based and the third is case-based. The common and major limitation of the first and the second reasonings is that they do not have automated learning ability. This paper presents an interactive and effective Case Based Decision Support System for online Diagnosis (CB-DSSD) of an APS. It offers a synergy between the Case Based Reasoning (CBR) and the Decision Support System (DSS) in order to support and assist Human Operator of Supervision (HOS) in his/her decision process. Indeed, the experimental evaluation performed on an Interactive Training System for PLC (ITS PLC) that allows the control of a Programmable Logic Controller (PLC), simulating sensors or/and actuators failures and validating the control algorithm through a real time interactive experience, showed the efficiency of our approach.

  4. Intelligent Case Based Decision Support System for Online Diagnosis of Automated Production System

    International Nuclear Information System (INIS)

    Ben Rabah, N; Saddem, R; Carre-Menetrier, V; Ben Hmida, F; Tagina, M

    2017-01-01

    Diagnosis of Automated Production System (APS) is a decision-making process designed to detect, locate and identify a particular failure caused by the control law. In the literature, there are three major types of reasoning for industrial diagnosis: the first is model-based, the second is rule-based and the third is case-based. The common and major limitation of the first and the second reasonings is that they do not have automated learning ability. This paper presents an interactive and effective Case Based Decision Support System for online Diagnosis (CB-DSSD) of an APS. It offers a synergy between the Case Based Reasoning (CBR) and the Decision Support System (DSS) in order to support and assist Human Operator of Supervision (HOS) in his/her decision process. Indeed, the experimental evaluation performed on an Interactive Training System for PLC (ITS PLC) that allows the control of a Programmable Logic Controller (PLC), simulating sensors or/and actuators failures and validating the control algorithm through a real time interactive experience, showed the efficiency of our approach. (paper)

  5. Conceptualising a system for quality clinical decision-making in ...

    African Journals Online (AJOL)

    As a feedback mechanism to promote or improve the quality of clinical decisions in nursing, standards for quality clinical decision-making are proposed in an exemplary manner. In addition, a system for quality clinical decisionmaking in nursing capitalises on the heritage of the nursing process. Considering the changes and ...

  6. An online infertility clinical decision support system

    Directory of Open Access Journals (Sweden)

    Fabio Diniz de Souza

    2017-09-01

    Full Text Available Objective: To explore some possibilities of computer applications in medicine, and to discuss an online infertility clinical decision support system. Methods: Retrospective data were obtained from 52 couples, and then entered into the online tool. Both its results and the initial diagnoses obtained by the treating physicians were compared with the final diagnoses established by laparoscopy and other diagnostic tests (semen analysis, hormone analysis, endometrial biopsy, ultrasound and hysteroscopy. The initial hypothesis of the research was that the online tool’s output was statistically associated with the final diagnoses. In order to verify that hypothesis, a chi-square (氈2 test with Yates’ correction for continuity (P<0.05 was performed to verify if the online tool’s and the doctor’s diagnoses were statistically associated with the final diagnoses. Results: Four etiological factors were present in more than 50% of the couples (ovarian, tubal-peritoneal, uterine, and endometriosis. The statistical results confirmed the research hypothesis for eight out of the nine etiological factors (ovarian, tubal-peritoneal, uterine, cervical, male, vaginal, psychosomatic, and endometriosis; P<0.05. Since there were no cases related to the immune factor in the sample, further clinical data are necessary in order to assess the online tool’s performance for that factor. Conclusions: The online tool tends to present more false-positives than false negatives, whereas the expert physician tends to present more false-negatives than false-positives. Therefore, the online tool and the doctor seem to complement each other. Finally, the obtained results suggest that the infertility online tool discussed herein might be a useful research and instructional tool.

  7. [Clinical judgment and decision, pedagogy and practice].

    Science.gov (United States)

    Lacronique, J F

    1987-01-01

    The interactive systems of logical interference represent but one of the computer applications to medicine. While the potential of computers in medical practice is beyond question, their actual use is not widespread. After the stage of practical demonstration of the working features of the hardware, one needs to define accurately the purpose to which the computer is intended in order to perform efficiently in its everyday use. To a certain extent, this unavoidable specialisation contrasts with the ubiquitous presence of computers and the availability of software the use of which does not, in principle, require particular training. A teaching experience directed to a number of different user groups in various fields has prompted us to examine the bases of the difficulties we met with. While some of them are related to cultural (or even religious) grounds, other, being of more technical nature, are more readily amendable to a methodological inquiry. Briefly, this analysis has led us to suggest a revision of various computer applications, including the interactive systems of logical interference, in the field of clinical research. A minimal theoretical training is essential in order to prevent delusions caused by an improvident autodidactic approach. The formal analysis of decision making appears as an excellent teaching guideline since it allows to refresh the elementary statistical concepts and then to approach economical aspects of health management (especially the cost/benefit and cost/effectiveness studies), as well as the sciences of administration as applied to health problems. Oncology represents a particularly suitable field of application on several accounts. It covers various and complex clinical domains, constant conceptual developments and finally, owing to the need for a systematic organisation of the data collection, it offers persuasive applications whose lasting features should warrant the necessary initial effort of investment.

  8. Computerised clinical decision support for suspected PE.

    Science.gov (United States)

    Jiménez, David; Resano, Santiago; Otero, Remedios; Jurkojc, Carolina; Portillo, Ana Karina; Ruiz-Artacho, Pedro; Corres, Jesús; Vicente, Agustina; den Exter, Paul L; Huisman, Menno V; Moores, Lisa; Yusen, Roger D

    2015-09-01

    This study aimed to determine the effect of an evidence-based clinical decision support (CDS) algorithm on the use and yield of CT pulmonary angiography (CTPA) and on outcomes of patients evaluated in the emergency department (ED) for suspected PE. The study included 1363 consecutive patients evaluated for suspected PE in an ED during 12 months before and 12 months after initiation of CDS use. Introduction of CDS was associated with decreased CTPA use (55% vs 49%; absolute difference (AD), 6.3%; 95% CI 1.0% to 11.6%; p=0.02). The use of CDS was associated with fewer symptomatic venous thromboembolic events during follow-up in patients with an initial negative diagnostic evaluation for PE (0.7% vs 3.2%; AD 2.5%; 95% CI 0.9% to 4.6%; p<0.01). Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  9. Fuzzy-Arden-Syntax-based, Vendor-agnostic, Scalable Clinical Decision Support and Monitoring Platform.

    Science.gov (United States)

    Adlassnig, Klaus-Peter; Fehre, Karsten; Rappelsberger, Andrea

    2015-01-01

    This study's objective is to develop and use a scalable genuine technology platform for clinical decision support based on Arden Syntax, which was extended by fuzzy set theory and fuzzy logic. Arden Syntax is a widely recognized formal language for representing clinical and scientific knowledge in an executable format, and is maintained by Health Level Seven (HL7) International and approved by the American National Standards Institute (ANSI). Fuzzy set theory and logic permit the representation of knowledge and automated reasoning under linguistic and propositional uncertainty. These forms of uncertainty are a common feature of patients' medical data, the body of medical knowledge, and deductive clinical reasoning.

  10. How to make the best decision. Philosophical aspects of clinical decision theory.

    Science.gov (United States)

    Wulff, H R

    1981-01-01

    An attempt is made to discuss some of the philosophical implications of the use of decision-analytic techniques. The probabilities of a decision analysis are subjective measures of belief, and it is concluded that clinicians base their subjective beliefs on both recorded observations and theoretical knowledge. The clinical decision maker also evaluates the consequences of his actions, and therefore clinical decision theory transcends medical science. A number of different schools of normative ethics are mentioned to illustrate the complexity of everyday decision making. The philosophical terminology is useful for the analysis of clinical problems, and it is argued that clinical decision making has both a teleological and a deontological component. The results of decision-analytic studies depend on such factors as the wealth of the country, the organization of the health service, and cultural norms.

  11. Automation of information decision support to improve e-learning resources quality

    Directory of Open Access Journals (Sweden)

    A.L. Danchenko

    2013-06-01

    Full Text Available Purpose. In conditions of active development of e-learning the high quality of e-learning resources is very important. Providing the high quality of e-learning resources in situation with mass higher education and rapid obsolescence of information requires the automation of information decision support for improving the quality of e-learning resources by development of decision support system. Methodology. The problem is solved by methods of artificial intelligence. The knowledge base of information structure of decision support system that is based on frame model of knowledge representation and inference production rules are developed. Findings. According to the results of the analysis of life cycle processes and requirements to the e-learning resources quality the information model of the structure of the knowledge base of the decision support system, the inference rules for the automatically generating of recommendations and the software implementation are developed. Practical value. It is established that the basic requirements for quality are performance, validity, reliability and manufacturability. It is shown that the using of a software implementation of decision support system for researched courses gives a growth of the quality according to the complex quality criteria. The information structure of a knowledge base system to support decision-making and rules of inference can be used by methodologists and content developers of learning systems.

  12. Failsafe automation of Phase II clinical trial interim monitoring for stopping rules.

    Science.gov (United States)

    Day, Roger S

    2010-02-01

    In Phase II clinical trials in cancer, preventing the treatment of patients on a study when current data demonstrate that the treatment is insufficiently active or too toxic has obvious benefits, both in protecting patients and in reducing sponsor costs. Considerable efforts have gone into experimental designs for Phase II clinical trials with flexible sample size, usually implemented by early stopping rules. The intended benefits will not ensue, however, if the design is not followed. Despite the best intentions, failures can occur for many reasons. The main goal is to develop an automated system for interim monitoring, as a backup system supplementing the protocol team, to ensure that patients are protected. A secondary goal is to stimulate timely recording of patient assessments. We developed key concepts and performance needs, then designed, implemented, and deployed a software solution embedded in the clinical trials database system. The system has been in place since October 2007. One clinical trial tripped the automated monitor, resulting in e-mails that initiated statistician/investigator review in timely fashion. Several essential contributing activities still require human intervention, institutional policy decisions, and institutional commitment of resources. We believe that implementing the concepts presented here will provide greater assurance that interim monitoring plans are followed and that patients are protected from inadequate response or excessive toxicity. This approach may also facilitate wider acceptance and quicker implementation of new interim monitoring algorithms.

  13. Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets.

    Science.gov (United States)

    Chen, Jonathan H; Alagappan, Muthuraman; Goldstein, Mary K; Asch, Steven M; Altman, Russ B

    2017-06-01

    Determine how varying longitudinal historical training data can impact prediction of future clinical decisions. Estimate the "decay rate" of clinical data source relevance. We trained a clinical order recommender system, analogous to Netflix or Amazon's "Customers who bought A also bought B..." product recommenders, based on a tertiary academic hospital's structured electronic health record data. We used this system to predict future (2013) admission orders based on different subsets of historical training data (2009 through 2012), relative to existing human-authored order sets. Predicting future (2013) inpatient orders is more accurate with models trained on just one month of recent (2012) data than with 12 months of older (2009) data (ROC AUC 0.91 vs. 0.88, precision 27% vs. 22%, recall 52% vs. 43%, all P<10 -10 ). Algorithmically learned models from even the older (2009) data was still more effective than existing human-authored order sets (ROC AUC 0.81, precision 16% recall 35%). Training with more longitudinal data (2009-2012) was no better than using only the most recent (2012) data, unless applying a decaying weighting scheme with a "half-life" of data relevance about 4 months. Clinical practice patterns (automatically) learned from electronic health record data can vary substantially across years. Gold standards for clinical decision support are elusive moving targets, reinforcing the need for automated methods that can adapt to evolving information. Prioritizing small amounts of recent data is more effective than using larger amounts of older data towards future clinical predictions. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  14. Automated syndrome detection in a set of clinical facial photographs.

    Science.gov (United States)

    Boehringer, Stefan; Guenther, Manuel; Sinigerova, Stella; Wurtz, Rolf P; Horsthemke, Bernhard; Wieczorek, Dagmar

    2011-09-01

    Computer systems play an important role in clinical genetics and are a routine part of finding clinical diagnoses but make it difficult to fully exploit information derived from facial appearance. So far, automated syndrome diagnosis based on digital, facial photographs has been demonstrated under study conditions but has not been applied in clinical practice. We have therefore investigated how well statistical classifiers trained on study data comprising 202 individuals affected by one of 14 syndromes could classify a set of 91 patients for whom pictures were taken under regular, less controlled conditions in clinical practice. We found a classification accuracy of 21% percent in the clinical sample representing a ratio of 3.0 over a random choice. This contrasts with a 60% accuracy or 8.5 ratio in the training data. Producing average images in both groups from sets of pictures for each syndrome demonstrates that the groups exhibit large phenotypic differences explaining discrepancies in accuracy. A broadening of the data set is suggested in order to improve accuracy in clinical practice. In order to further this goal, a software package is made available that allows application of the procedures and contributions toward an improved data set. Copyright © 2011 Wiley-Liss, Inc.

  15. Automated Clinical Assessment from Smart home-based Behavior Data

    Science.gov (United States)

    Dawadi, Prafulla Nath; Cook, Diane Joyce; Schmitter-Edgecombe, Maureen

    2016-01-01

    Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behaviour in the home and predicting standard clinical assessment scores of the residents. To accomplish this goal, we propose a Clinical Assessment using Activity Behavior (CAAB) approach to model a smart home resident’s daily behavior and predict the corresponding standard clinical assessment scores. CAAB uses statistical features that describe characteristics of a resident’s daily activity performance to train machine learning algorithms that predict the clinical assessment scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years using prediction and classification-based experiments. In the prediction-based experiments, we obtain a statistically significant correlation (r = 0.72) between CAAB-predicted and clinician-provided cognitive assessment scores and a statistically significant correlation (r = 0.45) between CAAB-predicted and clinician-provided mobility scores. Similarly, for the classification-based experiments, we find CAAB has a classification accuracy of 72% while classifying cognitive assessment scores and 76% while classifying mobility scores. These prediction and classification results suggest that it is feasible to predict standard clinical scores using smart home sensor data and learning-based data analysis. PMID:26292348

  16. Clinical Decision Making of Nurses Working in Hospital Settings

    OpenAIRE

    Bj?rk, Ida Torunn; Hamilton, Glenys A.

    2011-01-01

    This study analyzed nurses' perceptions of clinical decision making (CDM) in their clinical practice and compared differences in decision making related to nurse demographic and contextual variables. A cross-sectional survey was carried out with 2095 nurses in four hospitals in Norway. A 24-item Nursing Decision Making Instrument based on cognitive continuum theory was used to explore how nurses perceived their CDM when meeting an elective patient for the first time. Data were analyzed with d...

  17. Clinical models of decision making in addiction.

    Science.gov (United States)

    Koffarnus, Mikhail N; Kaplan, Brent A

    2018-01-01

    As research on decision making in addiction accumulates, it is increasingly clear that decision-making processes are dysfunctional in addiction and that this dysfunction may be fundamental to the initiation and maintenance of addictive behavior. How drug-dependent individuals value and choose among drug and nondrug rewards is consistently different from non-dependent individuals. The present review focuses on the assessment of decision-making in addiction. We cover the common behavioral tasks that have shown to be fruitful in decision-making research and highlight analytical and graphical considerations, when available, to facilitate comparisons within and among studies. Delay discounting tasks, drug demand tasks, drug choice tasks, the Iowa Gambling Task, and the Balloon Analogue Risk Task are included. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Multimedia abstract generation of intensive care data: the automation of clinical processes through AI methodologies.

    Science.gov (United States)

    Jordan, Desmond; Rose, Sydney E

    2010-04-01

    Medical errors from communication failures are enormous during the perioperative period of cardiac surgical patients. As caregivers change shifts or surgical patients change location within the hospital, key information is lost or misconstrued. After a baseline cognitive study of information need and caregiver workflow, we implemented an advanced clinical decision support tool of intelligent agents, medical logic modules, and text generators called the "Inference Engine" to summarize individual patient's raw medical data elements into procedural milestones, illness severity, and care therapies. The system generates two displays: 1) the continuum of care, multimedia abstract generation of intensive care data (MAGIC)-an expert system that would automatically generate a physician briefing of a cardiac patient's operative course in a multimodal format; and 2) the isolated point in time, "Inference Engine"-a system that provides a real-time, high-level, summarized depiction of a patient's clinical status. In our studies, system accuracy and efficacy was judged against clinician performance in the workplace. To test the automated physician briefing, "MAGIC," the patient's intraoperative course, was reviewed in the intensive care unit before patient arrival. It was then judged against the actual physician briefing and that given in a cohort of patients where the system was not used. To test the real-time representation of the patient's clinical status, system inferences were judged against clinician decisions. Changes in workflow and situational awareness were assessed by questionnaires and process evaluation. MAGIC provides 200% more information, twice the accuracy, and enhances situational awareness. This study demonstrates that the automation of clinical processes through AI methodologies yields positive results.

  19. Patient decision-making for clinical genetics.

    Science.gov (United States)

    Anderson, Gwen

    2007-03-01

    Medicine is incorporating genetic services into all avenues of health-care, ranging from the rarest to the most common diseases. Cognitive theories of decision-making still dominate professionals' understanding of patient decision-making about how to use genetic information and whether to have testing. I discovered a conceptual model of decision-making while carrying out a phenomenological-hermeneutic descriptive study of a convenience sample of 12 couples who were interviewed while deciding whether to undergo prenatal genetic testing. Thirty-two interviews were conducted with 12 men and 12 women separately. Interviews were transcribed verbatim and all data were analyzed using three levels of coding that were sorted into 30 categories and then abstracted into three emergent meta-themes that described men's and women's attempts to make sense and find meaning in how to best use prenatal genetic technology. Their descriptions of how they thought about, communicated, and coped with their decision were so detailed it was possible to discern nine different types of thinking they engaged in while deciding to accept or decline testing. They believed that decision-making is a process of working through your own personal style of thinking. This might include only one or any combination of the following types of thinking: analytical, ethical, moral, reflective, practical, hypothetical, judgmental, scary, and second sight, as described in detail by these 12 couples.

  20. Automated Segmentation of Coronary Arteries Based on Statistical Region Growing and Heuristic Decision Method

    Directory of Open Access Journals (Sweden)

    Yun Tian

    2016-01-01

    Full Text Available The segmentation of coronary arteries is a vital process that helps cardiovascular radiologists detect and quantify stenosis. In this paper, we propose a fully automated coronary artery segmentation from cardiac data volume. The method is built on a statistics region growing together with a heuristic decision. First, the heart region is extracted using a multi-atlas-based approach. Second, the vessel structures are enhanced via a 3D multiscale line filter. Next, seed points are detected automatically through a threshold preprocessing and a subsequent morphological operation. Based on the set of detected seed points, a statistics-based region growing is applied. Finally, results are obtained by setting conservative parameters. A heuristic decision method is then used to obtain the desired result automatically because parameters in region growing vary in different patients, and the segmentation requires full automation. The experiments are carried out on a dataset that includes eight-patient multivendor cardiac computed tomography angiography (CTA volume data. The DICE similarity index, mean distance, and Hausdorff distance metrics are employed to compare the proposed algorithm with two state-of-the-art methods. Experimental results indicate that the proposed algorithm is capable of performing complete, robust, and accurate extraction of coronary arteries.

  1. Clinical Decision Support: Statistical Hopes and Challenges

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan; Zvárová, Jana

    2016-01-01

    Roč. 4, č. 1 (2016), s. 30-34 ISSN 1805-8698 Grant - others:Nadační fond na opdporu vědy(CZ) Neuron Institutional support: RVO:67985807 Keywords : decision support * data mining * multivariate statistics * psychiatry * information based medicine Subject RIV: BB - Applied Statistics, Operational Research

  2. About the methodology of system synthesis of decision-makings and its procedures automation

    Directory of Open Access Journals (Sweden)

    D. P. Oleynikov

    2016-01-01

     the use of computer equipment, which require signifi cant time expenses on the development of appropriate solutions. Therefore, it was decided to develop an automated system to improve the effectiveness of the implementation of a number of methodology procedures.Results.During the study were identifi ed use cases of the system, in accordance with which were formed the conceptual and technical architecture of the system, highlights the key subsystems of the reference data, knowledge about the methods of decision-making and synthesis strategies, and identifi es their development tools. As the database used by the DBMS MS SQL Server, as the client side – Borland Delphi.Conclusion. Due to the high complexity of formalization of intellectual, creative, methodologies operations, the focus of automation is the support of conceptual analysis of the decisionmakings subject area and formation on its basis the knowledge base of intellectual operations together with their characteristic features, aimed to combine operations that make up the group of methods of synthesis strategies decision-making and implementation of the search function on the basis of a intellectual operations knowledge base.

  3. Critical care nurse practitioners and clinical nurse specialists interface patterns with computer-based decision support systems.

    Science.gov (United States)

    Weber, Scott

    2007-11-01

    The purposes of this review are to examine the types of clinical decision support systems in use and to identify patterns of how critical care advanced practice nurses (APNs) have integrated these systems into their nursing care patient management practices. The decision-making process itself is analyzed with a focus on how automated systems attempt to capture and reflect human decisional processes in critical care nursing, including how systems actually organize and process information to create outcome estimations based on patient clinical indicators and prognosis logarithms. Characteristics of APN clinicians and implications of these characteristics on decision system use, based on the body of decision system user research, are introduced. A review of the Medline, Ovid, CINAHL, and PubMed literature databases was conducted using "clinical decision support systems,"computerized clinical decision making," and "APNs"; an examination of components of several major clinical decision systems was also undertaken. Use patterns among APNs and other clinicians appear to vary; there is a need for original research to examine how APNs actually use these systems in their practices in critical care settings. Because APNs are increasingly responsible for admission to, and transfer from, critical care settings, more understanding is needed on how they interact with this technology and how they see automated decision systems impacting their practices. APNs who practice in critical care settings vary significantly in how they use the clinical decision systems that are in operation in their practice settings. These APNs must have an understanding of their use patterns with these systems and should critically assess whether their patient care decision making is affected by the technology.

  4. Automated Extraction of Substance Use Information from Clinical Texts.

    Science.gov (United States)

    Wang, Yan; Chen, Elizabeth S; Pakhomov, Serguei; Arsoniadis, Elliot; Carter, Elizabeth W; Lindemann, Elizabeth; Sarkar, Indra Neil; Melton, Genevieve B

    2015-01-01

    Within clinical discourse, social history (SH) includes important information about substance use (alcohol, drug, and nicotine use) as key risk factors for disease, disability, and mortality. In this study, we developed and evaluated a natural language processing (NLP) system for automated detection of substance use statements and extraction of substance use attributes (e.g., temporal and status) based on Stanford Typed Dependencies. The developed NLP system leveraged linguistic resources and domain knowledge from a multi-site social history study, Propbank and the MiPACQ corpus. The system attained F-scores of 89.8, 84.6 and 89.4 respectively for alcohol, drug, and nicotine use statement detection, as well as average F-scores of 82.1, 90.3, 80.8, 88.7, 96.6, and 74.5 respectively for extraction of attributes. Our results suggest that NLP systems can achieve good performance when augmented with linguistic resources and domain knowledge when applied to a wide breadth of substance use free text clinical notes.

  5. Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

    International Nuclear Information System (INIS)

    Hargrave, C; Deegan, T; Gibbs, A; Poulsen, M; Moores, M; Harden, F; Mengersen, K

    2014-01-01

    A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific subregions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

  6. A novel approach to sequence validating protein expression clones with automated decision making

    Directory of Open Access Journals (Sweden)

    Mohr Stephanie E

    2007-06-01

    Full Text Available Abstract Background Whereas the molecular assembly of protein expression clones is readily automated and routinely accomplished in high throughput, sequence verification of these clones is still largely performed manually, an arduous and time consuming process. The ultimate goal of validation is to determine if a given plasmid clone matches its reference sequence sufficiently to be "acceptable" for use in protein expression experiments. Given the accelerating increase in availability of tens of thousands of unverified clones, there is a strong demand for rapid, efficient and accurate software that automates clone validation. Results We have developed an Automated Clone Evaluation (ACE system – the first comprehensive, multi-platform, web-based plasmid sequence verification software package. ACE automates the clone verification process by defining each clone sequence as a list of multidimensional discrepancy objects, each describing a difference between the clone and its expected sequence including the resulting polypeptide consequences. To evaluate clones automatically, this list can be compared against user acceptance criteria that specify the allowable number of discrepancies of each type. This strategy allows users to re-evaluate the same set of clones against different acceptance criteria as needed for use in other experiments. ACE manages the entire sequence validation process including contig management, identifying and annotating discrepancies, determining if discrepancies correspond to polymorphisms and clone finishing. Designed to manage thousands of clones simultaneously, ACE maintains a relational database to store information about clones at various completion stages, project processing parameters and acceptance criteria. In a direct comparison, the automated analysis by ACE took less time and was more accurate than a manual analysis of a 93 gene clone set. Conclusion ACE was designed to facilitate high throughput clone sequence

  7. Decision aids for people considering taking part in clinical trials.

    Science.gov (United States)

    Gillies, Katie; Cotton, Seonaidh C; Brehaut, Jamie C; Politi, Mary C; Skea, Zoe

    2015-11-27

    Several interventions have been developed to promote informed consent for participants in clinical trials. However, many of these interventions focus on the content and structure of information (e.g. enhanced information or changes to the presentation format) rather than the process of decision making. Patient decision aids support a decision making process about medical options. Decision aids support the decision process by providing information about available options and their associated outcomes, alongside information that enables patients to consider what value they place on particular outcomes, and provide structured guidance on steps of decision making. They have been shown to be effective for treatment and screening decisions but evidence on their effectiveness in the context of informed consent for clinical trials has not been synthesised. To assess the effectiveness of decision aids for clinical trial informed consent compared to no intervention, standard information (i.e. usual practice) or an alternative intervention on the decision making process. We searched the following databases and to March 2015: Cochrane Central Register of Controlled Trials (CENTRAL), The Cochrane Library; MEDLINE (OvidSP) (from 1950); EMBASE (OvidSP) (from 1980); PsycINFO (OvidSP) (from 1806); ASSIA (ProQuest) (from 1987); WHO International Clinical Trials Registry Platform (ICTRP) (http://apps.who.int/trialsearch/); ClinicalTrials.gov; ISRCTN Register (http://www.controlled-trials.com/isrctn/). We also searched reference lists of included studies and relevant reviews. We contacted study authors and other experts. There were no language restrictions. We included randomised and quasi-randomised controlled trials comparing decision aids in the informed consent process for clinical trials alone, or in conjunction with standard information (such as written or verbal) or alongside alternative interventions (e.g. paper-based versus web-based decision aids). Included trials involved

  8. Clinical decision-making: physicians' preferences and experiences

    Directory of Open Access Journals (Sweden)

    White Martha

    2007-03-01

    Full Text Available Abstract Background Shared decision-making has been advocated; however there are relatively few studies on physician preferences for, and experiences of, different styles of clinical decision-making as most research has focused on patient preferences and experiences. The objectives of this study were to determine 1 physician preferences for different styles of clinical decision-making; 2 styles of clinical decision-making physicians perceive themselves as practicing; and 3 the congruence between preferred and perceived style. In addition we sought to determine physician perceptions of the availability of time in visits, and their role in encouraging patients to look for health information. Methods Cross-sectional survey of a nationally representative sample of U.S. physicians. Results 1,050 (53% response rate physicians responded to the survey. Of these, 780 (75% preferred to share decision-making with their patients, 142 (14% preferred paternalism, and 118 (11% preferred consumerism. 87% of physicians perceived themselves as practicing their preferred style. Physicians who preferred their patients to play an active role in decision-making were more likely to report encouraging patients to look for information, and to report having enough time in visits. Conclusion Physicians tend to perceive themselves as practicing their preferred role in clinical decision-making. The direction of the association cannot be inferred from these data; however, we suggest that interventions aimed at promoting shared decision-making need to target physicians as well as patients.

  9. Library Automation as a Source of Management Information. Papers presented at the Clinic on Library Applications of Data Processing (19th, Urbana, IL, April 25-28, 1982).

    Science.gov (United States)

    Lancaster, F. Wilfrid, Ed.

    Papers presented at the 19th Clinic on Library Applications of Data Processing represent a great variety, ranging from a tutorial on management information and decision support systems, through more philosophical discussions of the value of computer-derived information in library management, to studies of the use of automated systems as sources of…

  10. Bayesian networks for clinical decision support: A rational approach to dynamic decision-making under uncertainty

    NARCIS (Netherlands)

    Gerven, M.A.J. van

    2007-01-01

    This dissertation deals with decision support in the context of clinical oncology. (Dynamic) Bayesian networks are used as a framework for (dynamic) decision-making under uncertainty and applied to a variety of diagnostic, prognostic, and treatment problems in medicine. It is shown that the proposed

  11. Bayesian networks for clinical decision support: A rational approach to dynamic decision-making under uncertainty

    OpenAIRE

    Gerven, M.A.J. van

    2007-01-01

    This dissertation deals with decision support in the context of clinical oncology. (Dynamic) Bayesian networks are used as a framework for (dynamic) decision-making under uncertainty and applied to a variety of diagnostic, prognostic, and treatment problems in medicine. It is shown that the proposed models perform well in realistic settings

  12. The impact of simulation sequencing on perceived clinical decision making.

    Science.gov (United States)

    Woda, Aimee; Hansen, Jamie; Paquette, Mary; Topp, Robert

    2017-09-01

    An emerging nursing education trend is to utilize simulated learning experiences as a means to optimize competency and decision making skills. The purpose of this study was to examine differences in students' perception of clinical decision making and clinical decision making-related self-confidence and anxiety based on the sequence (order) in which they participated in a block of simulated versus hospital-based learning experiences. A quasi-experimental crossover design was used. Between and within group differences were found relative to self-confidence with the decision making process. When comparing groups, at baseline the simulation followed by hospital group had significantly higher self-confidence scores, however, at 14-weeks both groups were not significantly different. Significant within group differences were found in the simulation followed by hospital group only, demonstrating a significant decrease in clinical decision making related anxiety across the semester. Finally, there were no significant difference in; perceived clinical decision making within or between the groups at the two measurement points. Preliminary findings suggest that simulated learning experiences can be offered with alternating sequences without impacting the process, anxiety or confidence with clinical decision making. This study provides beginning evidence to guide curriculum development and allow flexibility based on student needs and available resources. Copyright © 2017. Published by Elsevier Ltd.

  13. An exploration of clinical decision making in mental health triage.

    Science.gov (United States)

    Sands, Natisha

    2009-08-01

    Mental health (MH) triage is a specialist area of clinical nursing practice that involves complex decision making. The discussion in this article draws on the findings of a Ph.D. study that involved a statewide investigation of the scope of MH triage nursing practice in Victoria, Australia. Although the original Ph.D. study investigated a number of core practices in MH triage, the focus of the discussion in this article is specifically on the findings related to clinical decision making in MH triage, which have not previously been published. The study employed an exploratory descriptive research design that used mixed data collection methods including a survey questionnaire (n = 139) and semistructured interviews (n = 21). The study findings related to decision making revealed a lack of empirically tested evidence-based decision-making frameworks currently in use to support MH triage nursing practice. MH triage clinicians in Australia rely heavily on clinical experience to underpin decision making and have little of knowledge of theoretical models for practice, such as methodologies for rating urgency. A key recommendation arising from the study is the need to develop evidence-based decision-making frameworks such as clinical guidelines to inform and support MH triage clinical decision making.

  14. An introduction to clinical decision analysis in ophthalmology

    Directory of Open Access Journals (Sweden)

    Korah Sanita

    1999-01-01

    Full Text Available Ophthalmologists are often confronted with difficult clinical management problems. In such cases, even published experience may be limited; consequently multiple, generally unproven management options are usually available. When placed in such situations, most of us decide on the most appropriate course of action based on intuition or (limited previous experience. In this article, we use examples to introduce the concept of decision analysis, a method of generating objective decisions for complex clinical problems.

  15. A Solution Generator Algorithm for Decision Making based Automated Negotiation in the Construction Domain

    Directory of Open Access Journals (Sweden)

    Arazi Idrus

    2017-12-01

    Full Text Available In this paper, we present our work-in-progress of a proposed framework for automated negotiation in the construction domain. The proposed framework enables software agents to conduct negotiations and autonomously make value-based decisions. The framework consists of three main components which are, solution generator algorithm, negotiation algorithm, and conflict resolution algorithm. This paper extends the discussion on the solution generator algorithm that enables software agents to generate solutions and rank them from 1st to nth solution for the negotiation stage of the operation. The solution generator algorithm consists of three steps which are, review solutions, rank solutions, and form ranked solutions. For validation purpose, we present a scenario that utilizes the proposed algorithm to rank solutions. The validation shows that the algorithm is promising, however, it also highlights the conflict between different parties that needs further negotiation action.

  16. Comparison of manual and automated size measurements of lung metastases on MDCT images: Potential influence on therapeutic decisions

    International Nuclear Information System (INIS)

    Pauls, Sandra; Kuerschner, Christian; Dharaiya, Ekta; Muche, Rainer; Schmidt, Stefan A.; Krueger, Stefan; Brambs, Hans-Juergen; Aschoff, Andrik J.

    2008-01-01

    Purpose: The goal of this study was to evaluate the influence of automated measurement of diameter, area, and volume from chest CT scans on therapeutic decisions of lung nodules as compared to manual 2-D measurements. Patients and method: The retrospective study involved 25 patients with 75 lung metastases. Contrast enhanced CT scans (16 row) of the lung were performed three times during chemotherapy with a mean time interval of 67.9 days between scans. In each patient, three metastases were evaluated (n = 225). Automatic measurements were compared to manual assessment for the following parameters: diameter, area, and density. The influence on the therapeutic decisions was evaluated using the RECIST criteria. Results: The maximum diameter measured by the automatic application was on an average 27% (S.D. 39; CI: 0.22-0.32; p < 0.0001) higher than the maximum diameter with manual assessment, and the differences depended on metastases size. Based on diameter calculation, manual and automated assessment disagreed in up to 32% of therapeutic decisions. Volumetric assessment tended towards more changes in therapy as compared to diameter calculation. The calculation of mean transversal area of metastases was 36% (S.D. 0.305; CI: -0.40 to -0.32; p < 0.0001) less with automated measurement. Therapeutic strategy would be changed in up to 25.7% of nodules using automated area calculation. Automated assessment of nodules' area and volume could influence the therapeutic decisions in up to 51.4% of all nodules. Density of the nodules was not validated to determine the influence on therapeutic decisions. Conclusion: There is a discrepancy between the manual and automated size measurement of lung metastases which could be significant

  17. Automated Extraction of Family History Information from Clinical Notes

    Science.gov (United States)

    Bill, Robert; Pakhomov, Serguei; Chen, Elizabeth S.; Winden, Tamara J.; Carter, Elizabeth W.; Melton, Genevieve B.

    2014-01-01

    Despite increased functionality for obtaining family history in a structured format within electronic health record systems, clinical notes often still contain this information. We developed and evaluated an Unstructured Information Management Application (UIMA)-based natural language processing (NLP) module for automated extraction of family history information with functionality for identifying statements, observations (e.g., disease or procedure), relative or side of family with attributes (i.e., vital status, age of diagnosis, certainty, and negation), and predication (“indicator phrases”), the latter of which was used to establish relationships between observations and family member. The family history NLP system demonstrated F-scores of 66.9, 92.4, 82.9, 57.3, 97.7, and 61.9 for detection of family history statements, family member identification, observation identification, negation identification, vital status, and overall extraction of the predications between family members and observations, respectively. While the system performed well for detection of family history statements and predication constituents, further work is needed to improve extraction of certainty and temporal modifications. PMID:25954443

  18. Syncope: risk stratification and clinical decision making.

    Science.gov (United States)

    Peeters, Suzanne Y G; Hoek, Amber E; Mollink, Susan M; Huff, J Stephen

    2014-04-01

    Syncope is a common occurrence in the emergency department, accounting for approximately 1% to 3% of presentations. Syncope is best defined as a brief loss of consciousness and postural tone followed by spontaneous and complete recovery. The spectrum of etiologies ranges from benign to life threatening, and a structured approach to evaluating these patients is key to providing care that is thorough, yet cost-effective. This issue reviews the most relevant evidence for managing and risk stratifying the syncope patient, beginning with a focused history, physical examination, electrocardiogram, and tailored diagnostic testing. Several risk stratification decision rules are compared for performance in various scenarios, including how age and associated comorbidities may predict short-term and long-term adverse events. An algorithm for structured, evidence-based care of the syncope patient is included to ensure that patients requiring hospitalization are managed appropriately and those with benign causes are discharged safely.

  19. Automated extraction of reported statistical analyses: towards a logical representation of clinical trial literature.

    Science.gov (United States)

    Hsu, William; Speier, William; Taira, Ricky K

    2012-01-01

    Randomized controlled trials are an important source of evidence for guiding clinical decisions when treating a patient. However, given the large number of studies and their variability in quality, determining how to summarize reported results and formalize them as part of practice guidelines continues to be a challenge. We have developed a set of information extraction and annotation tools to automate the identification of key information from papers related to the hypothesis, sample size, statistical test, confidence interval, significance level, and conclusions. We adapted the Automated Sequence Annotation Pipeline to map extracted phrases to relevant knowledge sources. We trained and tested our system on a corpus of 42 full-text articles related to chemotherapy of non-small cell lung cancer. On our test set of 7 papers, we obtained an overall precision of 86%, recall of 78%, and an F-score of 0.82 for classifying sentences. This work represents our efforts towards utilizing this information for quality assessment, meta-analysis, and modeling.

  20. The Feasibility of Sophisticated Multicriteria Support for Clinical Decisions.

    Science.gov (United States)

    Dolan, James G; Veazie, Peter J

    2017-10-01

    Multicriteria decision-making (MCDM) methods are well-suited to serve as the foundation for clinical decision support systems. To do so, however, they need to be appropriate for use in busy clinical settings. We compared decision-making processes and outcomes of patient-level analyses done with a range of multicriteria methods that vary in ease of use and intensity of decision support, 2 factors that could affect their ease of implementation into practice. We conducted a series of Internet surveys to compare the effects of 5 multicriteria methods that differ in user interface and required user input format on decisions regarding selection of a preferred method for lowering the risk of cardiovascular disease. The study sample consisted of members of an online Internet panel maintained by Fluidsurveys, an Internet survey company. Study outcomes were changes in preferred option, decision confidence, preparation for decision making, the Values Clarification and Decisional Uncertainty subscales of the Decisional Conflict Scale, and method ease of use. The frequency of changes in the preferred option ranged from 9% to 38%, P decision support provided by the MCDM method increased. The proportion of respondents who rated the method as easy ranged from 57% to 79% and differed significantly among MCDM methods, P = 0.003, but was not consistently related to intensity of decision support or ease of use. Decision support based on MCDM methods is not necessarily limited by decreases in ease of use. This result suggests that it is possible to develop decision support tools using sophisticated multicriteria techniques suitable for use in routine clinical care settings.

  1. Cognitive Elements in Clinical Decision-Making

    Science.gov (United States)

    Dunphy, Bruce C.; Cantwell, Robert; Bourke, Sid; Fleming, Mark; Smith, Bruce; Joseph, K. S.; Dunphy, Stacey L

    2010-01-01

    Physician cognition, metacognition and affect may have an impact upon the quality of clinical reasoning. The purpose of this study was to examine the relationship between measures of physician metacognition and affect and patient outcomes in obstetric practice. Reflective coping (RC), proactive coping, need for cognition (NFC), tolerance for…

  2. Decision Analysis for Metric Selection on a Clinical Quality Scorecard.

    Science.gov (United States)

    Guth, Rebecca M; Storey, Patricia E; Vitale, Michael; Markan-Aurora, Sumita; Gordon, Randolph; Prevost, Traci Q; Dunagan, Wm Claiborne; Woeltje, Keith F

    2016-09-01

    Clinical quality scorecards are used by health care institutions to monitor clinical performance and drive quality improvement. Because of the rapid proliferation of quality metrics in health care, BJC HealthCare found it increasingly difficult to select the most impactful scorecard metrics while still monitoring metrics for regulatory purposes. A 7-step measure selection process was implemented incorporating Kepner-Tregoe Decision Analysis, which is a systematic process that considers key criteria that must be satisfied in order to make the best decision. The decision analysis process evaluates what metrics will most appropriately fulfill these criteria, as well as identifies potential risks associated with a particular metric in order to identify threats to its implementation. Using this process, a list of 750 potential metrics was narrowed to 25 that were selected for scorecard inclusion. This decision analysis process created a more transparent, reproducible approach for selecting quality metrics for clinical quality scorecards. © The Author(s) 2015.

  3. Semi-automated entry of clinical temporal-abstraction knowledge.

    Science.gov (United States)

    Shahar, Y; Chen, H; Stites, D P; Basso, L V; Kaizer, H; Wilson, D M; Musen, M A

    1999-01-01

    The authors discuss the usability of an automated tool that supports entry, by clinical experts, of the knowledge necessary for forming high-level concepts and patterns from raw time-oriented clinical data. Based on their previous work on the RESUME system for forming high-level concepts from raw time-oriented clinical data, the authors designed a graphical knowledge acquisition (KA) tool that acquires the knowledge required by RESUME. This tool was designed using Protégé, a general framework and set of tools for the construction of knowledge-based systems. The usability of the KA tool was evaluated by three expert physicians and three knowledge engineers in three domains-the monitoring of children's growth, the care of patients with diabetes, and protocol-based care in oncology and in experimental therapy for AIDS. The study evaluated the usability of the KA tool for the entry of previously elicited knowledge. The authors recorded the time required to understand the methodology and the KA tool and to enter the knowledge; they examined the subjects' qualitative comments; and they compared the output abstractions with benchmark abstractions computed from the same data and a version of the same knowledge entered manually by RESUME experts. Understanding RESUME required 6 to 20 hours (median, 15 to 20 hours); learning to use the KA tool required 2 to 6 hours (median, 3 to 4 hours). Entry times for physicians varied by domain-2 to 20 hours for growth monitoring (median, 3 hours), 6 and 12 hours for diabetes care, and 5 to 60 hours for protocol-based care (median, 10 hours). An increase in speed of up to 25 times (median, 3 times) was demonstrated for all participants when the KA process was repeated. On their first attempt at using the tool to enter the knowledge, the knowledge engineers recorded entry times similar to those of the expert physicians' second attempt at entering the same knowledge. In all cases RESUME, using knowledge entered by means of the KA tool

  4. Advances In Infection Surveillance and Clinical Decision Support With Fuzzy Sets and Fuzzy Logic.

    Science.gov (United States)

    Koller, Walter; de Bruin, Jeroen S; Rappelsberger, Andrea; Adlassnig, Klaus-Peter

    2015-01-01

    By the use of extended intelligent information technology tools for fully automated healthcare-associated infection (HAI) surveillance, clinicians can be informed and alerted about the emergence of infection-related conditions in their patients. Moni--a system for monitoring nosocomial infections in intensive care units for adult and neonatal patients--employs knowledge bases that were written with extensive use of fuzzy sets and fuzzy logic, allowing the inherent un-sharpness of clinical terms and the inherent uncertainty of clinical conclusions to be a part of Moni's output. Thus, linguistic as well as propositional uncertainty became a part of Moni, which can now report retrospectively on HAIs according to traditional crisp HAI surveillance definitions, as well as support clinical bedside work by more complex crisp and fuzzy alerts and reminders. This improved approach can bridge the gap between classical retrospective surveillance of HAIs and ongoing prospective clinical-decision-oriented HAI support.

  5. Quantitative Analysis of Uncertainty in Medical Reporting: Part 3: Customizable Education, Decision Support, and Automated Alerts.

    Science.gov (United States)

    Reiner, Bruce I

    2017-12-18

    In order to better elucidate and understand the causative factors and clinical implications of uncertainty in medical reporting, one must first create a referenceable database which records a number of standardized metrics related to uncertainty language, clinical context, technology, and provider and patient data. The resulting analytics can in turn be used to create context and user-specific reporting guidelines, real-time decision support, educational resources, and quality assurance measures. If this technology can be directly integrated into reporting technology and workflow, the goal is to proactively improve clinical outcomes at the point of care.

  6. The impact of an electronic clinical decision support for pulmonary ...

    African Journals Online (AJOL)

    State-of-the-art electronic radiology workflow can provide clinical decision support (CDS) for specialised imaging requests, but there has been limited work on the clinical impact of CDS in PE, particularly in resource-constrained environments. Objective. To determine the impact of an electronic CDS for PE on the efficiency ...

  7. Clinical decision making of nurses regarding elder abuse.

    Science.gov (United States)

    Meeks-Sjostrom, Diana J

    2013-01-01

    A descriptive correlational design was used to examine the clinical decision making of nurses regarding elder abuse. The relationship of the nurses' applied knowledge of elder abuse, years of experience as a Registered Nurse (RN), clinical level of practice status, the use of intuition, and clinical decision outcomes for patients in cases of suspected elder abuse were examined. The convenience sample of 84 RNs consisted of 68 females and 16 males. Results indicated an overall model of two predictors that significantly predicted outcomes. The t-test revealed no difference between RNs who received elder abuse education and those who did not.

  8. Making smart investment decisions in clinical research.

    Science.gov (United States)

    Bansback, Nick; Keystone, Edward; O'Dell, James; Phibbs, Ciaran S; Hannagan, Keri; Brophy, Mary; Anis, Aslam

    2015-12-29

    A recent trial in rheumatoid arthritis found an inexpensive, but infrequently used, combination of therapies is neither inferior nor less safe than an expensive biologic drug. If the trial had been conducted over 10 years ago, arguably 100's of millions of dollars since spent on biologics could have been released to other, more effective treatments. Given the ever increasing number of trials proposed, this commentary uses the trial as an example to challenge payers and research funders to make smarter investments in clinical research to save potential future costs. NCT00405275 , registered 29 November 2006.

  9. Clinical Chemistry Laboratory Automation in the 21st Century - Amat Victoria curam (Victory loves careful preparation)

    Science.gov (United States)

    Armbruster, David A; Overcash, David R; Reyes, Jaime

    2014-01-01

    The era of automation arrived with the introduction of the AutoAnalyzer using continuous flow analysis and the Robot Chemist that automated the traditional manual analytical steps. Successive generations of stand-alone analysers increased analytical speed, offered the ability to test high volumes of patient specimens, and provided large assay menus. A dichotomy developed, with a group of analysers devoted to performing routine clinical chemistry tests and another group dedicated to performing immunoassays using a variety of methodologies. Development of integrated systems greatly improved the analytical phase of clinical laboratory testing and further automation was developed for pre-analytical procedures, such as sample identification, sorting, and centrifugation, and post-analytical procedures, such as specimen storage and archiving. All phases of testing were ultimately combined in total laboratory automation (TLA) through which all modules involved are physically linked by some kind of track system, moving samples through the process from beginning-to-end. A newer and very powerful, analytical methodology is liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). LC-MS/MS has been automated but a future automation challenge will be to incorporate LC-MS/MS into TLA configurations. Another important facet of automation is informatics, including middleware, which interfaces the analyser software to a laboratory information systems (LIS) and/or hospital information systems (HIS). This software includes control of the overall operation of a TLA configuration and combines analytical results with patient demographic information to provide additional clinically useful information. This review describes automation relevant to clinical chemistry, but it must be recognised that automation applies to other specialties in the laboratory, e.g. haematology, urinalysis, microbiology. It is a given that automation will continue to evolve in the clinical laboratory

  10. Clinical Chemistry Laboratory Automation in the 21st Century - Amat Victoria curam (Victory loves careful preparation).

    Science.gov (United States)

    Armbruster, David A; Overcash, David R; Reyes, Jaime

    2014-08-01

    The era of automation arrived with the introduction of the AutoAnalyzer using continuous flow analysis and the Robot Chemist that automated the traditional manual analytical steps. Successive generations of stand-alone analysers increased analytical speed, offered the ability to test high volumes of patient specimens, and provided large assay menus. A dichotomy developed, with a group of analysers devoted to performing routine clinical chemistry tests and another group dedicated to performing immunoassays using a variety of methodologies. Development of integrated systems greatly improved the analytical phase of clinical laboratory testing and further automation was developed for pre-analytical procedures, such as sample identification, sorting, and centrifugation, and post-analytical procedures, such as specimen storage and archiving. All phases of testing were ultimately combined in total laboratory automation (TLA) through which all modules involved are physically linked by some kind of track system, moving samples through the process from beginning-to-end. A newer and very powerful, analytical methodology is liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). LC-MS/MS has been automated but a future automation challenge will be to incorporate LC-MS/MS into TLA configurations. Another important facet of automation is informatics, including middleware, which interfaces the analyser software to a laboratory information systems (LIS) and/or hospital information systems (HIS). This software includes control of the overall operation of a TLA configuration and combines analytical results with patient demographic information to provide additional clinically useful information. This review describes automation relevant to clinical chemistry, but it must be recognised that automation applies to other specialties in the laboratory, e.g. haematology, urinalysis, microbiology. It is a given that automation will continue to evolve in the clinical laboratory

  11. Clinical decision making of nurses working in hospital settings.

    Science.gov (United States)

    Bjørk, Ida Torunn; Hamilton, Glenys A

    2011-01-01

    This study analyzed nurses' perceptions of clinical decision making (CDM) in their clinical practice and compared differences in decision making related to nurse demographic and contextual variables. A cross-sectional survey was carried out with 2095 nurses in four hospitals in Norway. A 24-item Nursing Decision Making Instrument based on cognitive continuum theory was used to explore how nurses perceived their CDM when meeting an elective patient for the first time. Data were analyzed with descriptive frequencies, t-tests, Chi-Square test, and linear regression. Nurses' decision making was categorized into analytic-systematic, intuitive-interpretive, and quasi-rational models of CDM. Most nurses reported the use of quasi-rational models during CDM thereby supporting the tenet that cognition most often includes properties of both analysis and intuition. Increased use of intuitive-interpretive models of CDM was associated with years in present job, further education, male gender, higher age, and working in predominantly surgical units.

  12. Automated patient and medication payment method for clinical trials

    Directory of Open Access Journals (Sweden)

    Yawn BP

    2013-01-01

    Full Text Available Barbara P Yawn,1 Suzanne Madison,1 Susan Bertram,1 Wilson D Pace,2 Anne Fuhlbrigge,3 Elliot Israel,3 Dawn Littlefield,1 Margary Kurland,1 Michael E Wechsler41Olmsted Medical Center, Department of Research, Rochester, MN, 2UCDHSC, Department of Family Medicine, University of Colorado Health Science Centre, Aurora, CO, 3Brigham and Women's Hospital, Pulmonary and Critical Care Division, Boston, MA, 4National Jewish Medical Center, Division of Pulmonology, Denver, CO, USABackground: Published reports and studies related to patient compensation for clinical trials focus primarily on the ethical issues related to appropriate amounts to reimburse for patient's time and risk burden. Little has been published regarding the method of payment for patient participation. As clinical trials move into widely dispersed community practices and more complex designs, the method of payment also becomes more complex. Here we review the decision process and payment method selected for a primary care-based randomized clinical trial of asthma management in Black Americans.Methods: The method selected is a credit card system designed specifically for clinical trials that allows both fixed and variable real-time payments. We operationalized the study design by providing each patient with two cards, one for reimbursement for study visits and one for payment of medication costs directly to the pharmacies.Results: Of the 1015 patients enrolled, only two refused use of the ClinCard, requesting cash payments for visits and only rarely a weekend or fill-in pharmacist refused to use the card system for payment directly to the pharmacy. Overall, the system has been well accepted by patients and local study teams. The ClinCard administrative system facilitates the fiscal accounting and medication adherence record-keeping by the central teams. Monthly fees are modest, and all 12 study institutional review boards approved use of the system without concern for patient

  13. The Right to Explanation and the Right to Secrecy – Reconciling Data Protection and Trade Secret Rights in Automated Decision-making

    OpenAIRE

    Gunst, Helena

    2017-01-01

    The decisions of everyday life are to an increasing extent made by a new deciding force: the proprietary algorithm. The information society of today is a “world of automatic decision-making”, as more and more decisions are delegated to automatic systems which are able to process data and make decisions, often with little supervision from human decision-makers. Such automated decision-making presents an interesting dichotomy of interests: the companies that develop the algorithms and data proc...

  14. Clinical Decision Support with Guidelines and Bayesian Networks

    OpenAIRE

    Nee, Oliver; Hein, Andreas

    2010-01-01

    In this chapter the state of the art of decision support in medicine along with clinical guidelines has been described. Especially in fields of medicine where workflows are highly standardized and do not depend in a great extend on intra-individual variations, clinical guidelines are well accepted. In these fields the clinician can benefit from computer assistance. The implementation of a CDSS requires on one hand the formalization of the clinical guidelines and mechanisms for reasoning. This...

  15. Decision-theoretic planning of clinical patient management

    OpenAIRE

    Peek, Niels Bastiaan

    2000-01-01

    When a doctor is treating a patient, he is constantly facing decisions. From the externally visible signs and symptoms he must establish a hypothesis of what might be wrong with the patient; then he must decide whether additional diagnostic procedures are required to verify this hypothesis, whether therapeutic action is necessary, and which post-therapeutic trajectory is to be followed. All these bedside decisions are related to each other, and the whole task of clinical patient management ca...

  16. Toward Fully Automated Multicriterial Plan Generation: A Prospective Clinical Study

    Energy Technology Data Exchange (ETDEWEB)

    Voet, Peter W.J., E-mail: p.voet@erasmusmc.nl [Department of Radiation Oncology, Erasmus Medical Center–Daniel den Hoed Cancer Center, Groene Hilledijk 301, Rotterdam 3075EA (Netherlands); Dirkx, Maarten L.P.; Breedveld, Sebastiaan; Fransen, Dennie; Levendag, Peter C.; Heijmen, Ben J.M. [Department of Radiation Oncology, Erasmus Medical Center–Daniel den Hoed Cancer Center, Groene Hilledijk 301, Rotterdam 3075EA (Netherlands)

    2013-03-01

    Purpose: To prospectively compare plans generated with iCycle, an in-house-developed algorithm for fully automated multicriterial intensity modulated radiation therapy (IMRT) beam profile and beam orientation optimization, with plans manually generated by dosimetrists using the clinical treatment planning system. Methods and Materials: For 20 randomly selected head-and-neck cancer patients with various tumor locations (of whom 13 received sequential boost treatments), we offered the treating physician the choice between an automatically generated iCycle plan and a manually optimized plan using standard clinical procedures. Although iCycle used a fixed “wish list” with hard constraints and prioritized objectives, the dosimetrists manually selected the beam configuration and fine tuned the constraints and objectives for each IMRT plan. Dosimetrists were not informed in advance whether a competing iCycle plan was made. The 2 plans were simultaneously presented to the physician, who then selected the plan to be used for treatment. For the patient group, differences in planning target volume coverage and sparing of critical tissues were quantified. Results: In 32 of 33 plan comparisons, the physician selected the iCycle plan for treatment. This highly consistent preference for the automatically generated plans was mainly caused by the improved sparing for the large majority of critical structures. With iCycle, the normal tissue complication probabilities for the parotid and submandibular glands were reduced by 2.4% ± 4.9% (maximum, 18.5%, P=.001) and 6.5% ± 8.3% (maximum, 27%, P=.005), respectively. The reduction in the mean oral cavity dose was 2.8 ± 2.8 Gy (maximum, 8.1 Gy, P=.005). For the swallowing muscles, the esophagus and larynx, the mean dose reduction was 3.3 ± 1.1 Gy (maximum, 9.2 Gy, P<.001). For 15 of the 20 patients, target coverage was also improved. Conclusions: In 97% of cases, automatically generated plans were selected for treatment because of

  17. Implications of caries diagnostic strategies for clinical management decisions

    DEFF Research Database (Denmark)

    Baelum, Vibeke; Hintze, Hanne; Wenzel, Ann

    2012-01-01

    OBJECTIVES: In clinical practice, a visual-tactile caries examination is frequently supplemented by bitewing radiography. This study evaluated strategies for combining visual-tactile and radiographic caries detection methods and determined their implications for clinical management decisions...... that the visual-tactile method alone was the superior strategy, resulting in most correct clinical management decisions and most correct decisions regarding the choice of treatment.......-specificity) were calculated for each diagnostic strategy. RESULTS: Visual-tactile examination provided a true-positive rate of 34.2% and a false-positive rate of 1.5% for the detection of a cavity. The combination of a visual-tactile and a radiographic examination using the lesion in dentin threshold...

  18. Improving clinical decision support using data mining techniques

    Science.gov (United States)

    Burn-Thornton, Kath E.; Thorpe, Simon I.

    1999-02-01

    Physicians, in their ever-demanding jobs, are looking to decision support systems for aid in clinical diagnosis. However, clinical decision support systems need to be of sufficiently high accuracy that they help, rather than hinder, the physician in his/her diagnosis. Decision support systems with accuracies, of patient state determination, of greater than 80 percent, are generally perceived to be sufficiently accurate to fulfill the role of helping the physician. We have previously shown that data mining techniques have the potential to provide the underpinning technology for clinical decision support systems. In this paper, an extension of the work in reverence 2, we describe how changes in data mining methodologies, for the analysis of 12-lead ECG data, improve the accuracy by which data mining algorithms determine which patients are suffering from heart disease. We show that the accuracy of patient state prediction, for all the algorithms, which we investigated, can be increased by up to 6 percent, using the combination of appropriate test training ratios and 5-fold cross-validation. The use of cross-validation greater than 5-fold, appears to reduce the improvement in algorithm classification accuracy gained by the use of this validation method. The accuracy of 84 percent in patient state predictions, obtained using the algorithm OCI, suggests that this algorithm will be capable of providing the required accuracy for clinical decision support systems.

  19. The potential conflict between clinical and judicial decision making heuristics.

    Science.gov (United States)

    Rassin, E; Merckelbach, H

    1999-01-01

    The Gudjonsson Suggestibility Scale (GSS; Gudjonsson, 1984) was introduced as a tool for identifying suspects who are at risk of making false confessions. High GSS-scores indicate a greater risk of making false confessions. Recently, some authors have claimed that low GSS-scores can be used to support the credibility of recovered memories. This new application broadens the use of the GSS in two ways. First, low GSS-scores are considered to possess diagnostic value. Second, the GSS is advocated as a practical tool in clinical settings. This article critically evaluates such a clinical application of the GSS. Our main argument has to do with the incompatibility of basic clinical and judicial decision making heuristics. Psychotherapists, and other medical professionals, should base their decisions on different parameters than judicial professionals. Compared to judicial heuristics, clinical heuristics can be characterized as more empathetic, less critical, and less conservative. Given these differences, clinical conclusions (including those about the accuracy of recovered memories) cannot be easily translated into judicial decisions. If they do enter the judicial domain, these conclusions may lead to dubious forensic decisions. Copyright 1999 John Wiley & Sons, Ltd.

  20. The role of emotions in clinical reasoning and decision making.

    Science.gov (United States)

    Marcum, James A

    2013-10-01

    What role, if any, should emotions play in clinical reasoning and decision making? Traditionally, emotions have been excluded from clinical reasoning and decision making, but with recent advances in cognitive neuropsychology they are now considered an important component of them. Today, cognition is thought to be a set of complex processes relying on multiple types of intelligences. The role of mathematical logic (hypothetico-deductive thinking) or verbal linguistic intelligence in cognition, for example, is well documented and accepted; however, the role of emotional intelligence has received less attention-especially because its nature and function are not well understood. In this paper, I argue for the inclusion of emotions in clinical reasoning and decision making. To that end, developments in contemporary cognitive neuropsychology are initially examined and analyzed, followed by a review of the medical literature discussing the role of emotions in clinical practice. Next, a published clinical case is reconstructed and used to illustrate the recognition and regulation of emotions played during a series of clinical consultations, which resulted in a positive medical outcome. The paper's main thesis is that emotions, particularly in terms of emotional intelligence as a practical form of intelligence, afford clinical practitioners a robust cognitive resource for providing quality medical care.

  1. Automating Clinical Score Calculation within the Electronic Health Record. A Feasibility Assessment.

    Science.gov (United States)

    Aakre, Christopher; Dziadzko, Mikhail; Keegan, Mark T; Herasevich, Vitaly

    2017-04-12

    Evidence-based clinical scores are used frequently in clinical practice, but data collection and data entry can be time consuming and hinder their use. We investigated the programmability of 168 common clinical calculators for automation within electronic health records. We manually reviewed and categorized variables from 168 clinical calculators as being extractable from structured data, unstructured data, or both. Advanced data retrieval methods from unstructured data sources were tabulated for diagnoses, non-laboratory test results, clinical history, and examination findings. We identified 534 unique variables, of which 203/534 (37.8%) were extractable from structured data and 269/534 (50.4.7%) were potentially extractable using advanced techniques. Nearly half (265/534, 49.6%) of all variables were not retrievable. Only 26/168 (15.5%) of scores were completely programmable using only structured data and 43/168 (25.6%) could potentially be programmable using widely available advanced information retrieval techniques. Scores relying on clinical examination findings or clinical judgments were most often not completely programmable. Complete automation is not possible for most clinical scores because of the high prevalence of clinical examination findings or clinical judgments - partial automation is the most that can be achieved. The effect of fully or partially automated score calculation on clinical efficiency and clinical guideline adherence requires further study.

  2. Risk perception and clinical decision making in primary care

    DEFF Research Database (Denmark)

    Barfoed, Benedicte Marie Lind

    2015-01-01

    Objectives We aim to present new knowledge about different perspectives of health care professionals’ risk perceptions and clinical decision making. Furthermore, we intend to discuss differences between professional and personal risk perceptions and the impact on decisions in terms of both short...... and long-term outcomes. Background Insight into healthcare professionals’ perception of risk is a cornerstone for understanding their strategies for practising preventive care. The way people perceive risk can be seen as part of a general personality trait influenced by a mixture of individual...... considerations and the specific context. Most research has been focused on understanding of the concepts of risk. However healthcare professionals’ risk perception and personal attitudes also affect their clinical decision-making and risk communication. The differences between health care professionals’ personal...

  3. Decision making in clinical veterinary practice | Anene | Nigerian ...

    African Journals Online (AJOL)

    Decision making in clinical veterinary practice. BM Anene. Abstract. No Abstract. Full Text: EMAIL FREE FULL TEXT EMAIL FREE FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT · AJOL African Journals Online. HOW TO USE AJOL... for Researchers · for Librarians · for Authors · FAQ's · More about AJOL ...

  4. Hubble: Linked Data Hub for Clinical Decision Support

    NARCIS (Netherlands)

    Hoekstra, R.; Magliacane, S.; Rietveld, L.; de Vries, G.; Wibisono, A.; Schlobach, S.; Simperl, E.; Norton, B.; Mladenic, D.; Della Valle, E.; Fundulaki, I.; Passant, A.; Troncy, R.

    2015-01-01

    The AERS datasets is one of the few remaining, large publicly available medical data sets that until now have not been published as Linked Data. It is uniquely positioned amidst other medical datasets. This paper describes the Hubble prototype system for clinical decision support that demonstrates

  5. Impact of Medical Library Services on Clinical Decision-Making ...

    African Journals Online (AJOL)

    Relationship does not, however, exist between use of library “AND” service delivery. Generally, the library impacted on the Doctors' clinical decision-making despite its huge limitations enumerated by the Doctors. Recommendations were made towards a balanced collection development of both print and non-print materials ...

  6. Competence and decision-making: Ethics and clinical psychiatric ...

    African Journals Online (AJOL)

    The making of decisions pertaining to health and personal issues is dependent on the ability of the patient to function in various areas. The concept of competence is viewed differently from the clinical as opposed to the legal viewpoint. Some jurisdictions have introduced into legislation more specific legal guidelines for ...

  7. Incorporating clinical guidelines through clinician decision-making

    Directory of Open Access Journals (Sweden)

    Moore Brent A

    2008-02-01

    Full Text Available Abstract Background It is generally acknowledged that a disparity between knowledge and its implementation is adversely affecting quality of care. An example commonly cited is the failure of clinicians to follow clinical guidelines. A guiding assumption of this view is that adherence should be gauged by a standard of conformance. At least some guideline developers dispute this assumption and claim that their efforts are intended to inform and assist clinical practice, not to function as standards of performance. However, their ability to assist and inform will remain limited until an alternative to the conformance criterion is proposed that gauges how evidence-based guidelines are incorporated into clinical decisions. Methods The proposed investigation has two specific aims to identify the processes that affect decisions about incorporating clinical guidelines, and then to develop ad test a strategy that promotes the utilization of evidence-based practices. This paper focuses on the first aim. It presents the rationale, introduces the clinical paradigm of treatment-resistant schizophrenia, and discusses an exemplar of clinician non-conformance to a clinical guideline. A modification of the original study is proposed that targets psychiatric trainees and draws on a cognitively rich theory of decision-making to formulate hypotheses about how the guideline is incorporated into treatment decisions. Twenty volunteer subjects recruited from an accredited psychiatry training program will respond to sixty-four vignettes that represent a fully crossed 2 × 2 × 2 × 4 within-subjects design. The variables consist of criteria contained in the clinical guideline and other relevant factors. Subjects will also respond to a subset of eight vignettes that assesses their overall impression of the guideline. Generalization estimating equation models will be used to test the study's principal hypothesis and perform secondary analyses. Implications The original

  8. Standards in clinical decision support: activities in health level seven.

    Science.gov (United States)

    Jenders, Robert A; Jenders, Robert Allen; Del Fiol, Guilherme; Kawamoto, Kensaku; Sailors, R Matthew

    2008-11-06

    Health Level Seven (HL7) has evolved into an international standards development organization (SDO) with a suite of standards. Prominent among these are formalisms related to clinical decision support, including the Arden Syntax, GELLO and Decision Support Service (DSS) standards. Continuing improvement in these standards and ongoing development of future decision support standards require wide participation in order to maximize their success. Accordingly, the purpose of the workshop is twofold. First, instructors will convey the latest developments regarding existing decision support standards and related efforts to develop new standards. Second, the instructors will solicit feedback so that attendees who do not participate in HL7 can have input into the standards activities of that organization. The instructors of this workshop, who are the co-chairs and/or members of the Clinical Decision Support Technical Committee of HL7, will review progress in these areas. They will present the details of the ongoing development of the extant Arden Syntax, GELLO and DSS standards. They will discuss work on current draft and proposed future standards, including the Infobutton communication and Order Set standards that are undergoing development in anticipation of certification as standards. Finally, they will solicit discussion regarding the future direction of standards development in these areas.

  9. Clinical decisions for anterior restorations: the concept of restorative volume.

    Science.gov (United States)

    Cardoso, Jorge André; Almeida, Paulo Júlio; Fischer, Alex; Phaxay, Somano Luang

    2012-12-01

    The choice of the most appropriate restoration for anterior teeth is often a difficult decision. Numerous clinical and technical factors play an important role in selecting the treatment option that best suits the patient and the restorative team. Experienced clinicians have developed decision processes that are often more complex than may seem. Less experienced professionals may find difficulties making treatment decisions because of the widely varied restorative materials available and often numerous similar products offered by different manufacturers. The authors reviewed available evidence and integrated their clinical experience to select relevant factors that could provide a logical and practical guideline for restorative decisions in anterior teeth. The presented concept of restorative volume is based on structural, optical, and periodontal factors. Each of these factors will influence the short- and long-term behavior of restorations in terms of esthetics, biology, and function. Despite the marked evolution of esthetic restorative techniques and materials, significant limitations still exist, which should be addressed by researchers. The presented guidelines must be regarded as a mere orientation for risk analysis. A comprehensive individual approach should always be the core of restorative esthetic treatments. The complex decision process for anterior esthetic restorations can be clarified by a systematized examination of structural, optical, and periodontal factors. The basis for the proposed thought process is the concept of restorative volume that is a contemporary interpretation of restoration categories and their application. © 2012 Wiley Periodicals, Inc.

  10. Advancing clinical decision support using lessons from outside of healthcare: an interdisciplinary systematic review

    Directory of Open Access Journals (Sweden)

    Wu Helen W

    2012-08-01

    Full Text Available Abstract Background Greater use of computerized decision support (DS systems could address continuing safety and quality problems in healthcare, but the healthcare field has struggled to implement DS technology. This study surveys DS experience across multiple non-healthcare disciplines for new insights that are generalizable to healthcare provider decisions. In particular, it sought design principles and lessons learned from the other disciplines that could inform efforts to accelerate the adoption of clinical decision support (CDS. Methods Our systematic review drew broadly from non-healthcare databases in the basic sciences, social sciences, humanities, engineering, business, and defense: PsychINFO, BusinessSource Premier, Social Sciences Abstracts, Web of Science, and Defense Technical Information Center. Because our interest was in DS that could apply to clinical decisions, we selected articles that (1 provided a review, overview, discussion of lessons learned, or an evaluation of design or implementation aspects of DS within a non-healthcare discipline and (2 involved an element of human judgment at the individual level, as opposed to decisions that can be fully automated or that are made at the organizational level. Results Clinical decisions share some similarities with decisions made by military commanders, business managers, and other leaders: they involve assessing new situations and choosing courses of action with major consequences, under time pressure, and with incomplete information. We identified seven high-level DS system design features from the non-healthcare literature that could be applied to CDS: providing broad, system-level perspectives; customizing interfaces to specific users and roles; making the DS reasoning transparent; presenting data effectively; generating multiple scenarios covering disparate outcomes (e.g., effective; effective with side effects; ineffective; allowing for contingent adaptations; and facilitating

  11. Nomenclature and basic concepts in automation in the clinical laboratory setting: a practical glossary.

    Science.gov (United States)

    Evangelopoulos, Angelos A; Dalamaga, Maria; Panoutsopoulos, Konstantinos; Dima, Kleanthi

    2013-01-01

    In the early 80s, the word automation was used in the clinical laboratory setting referring only to analyzers. But in late 80s and afterwards, automation found its way into all aspects of the diagnostic process, embracing not only the analytical but also the pre- and post-analytical phase. While laboratories in the eastern world, mainly Japan, paved the way for laboratory automation, US and European laboratories soon realized the benefits and were quick to follow. Clearly, automation and robotics will be a key survival tool in a very competitive and cost-concious healthcare market. What sets automation technology apart from so many other efficiency solutions are the dramatic savings that it brings to the clinical laboratory. Further standardization will assure the success of this revolutionary new technology. One of the main difficulties laboratory managers and personnel must deal with when studying solutions to reengineer a laboratory is familiarizing themselves with the multidisciplinary and technical terminology of this new and exciting field. The present review/glossary aims at giving an overview of the most frequently used terms within the scope of laboratory automation and to put laboratory automation on a sounder linguistic basis.

  12. Discounting and risk characteristics in clinical decision-making.

    Science.gov (United States)

    Ortendahl, Monica; Fries, James F

    2006-03-01

    Time-related aspects have attracted an increasing interest in medical decisions. Health promotion often works toward remote goals, and many clinical judgments and decisions include an exchange of costs today for benefits in the future. The concept of diminishing value over time is positive discounting when the benefits occur so far in the future that they seem of little value relative to the immediate cost. If there is a preference to live for the present rather than save for the future, such a preference might not contribute to good health according to a lower discount rate. As discounting is related to risk an analysis of uncertainty is required being an unavoidable condition in health work. Shared decision-making between doctor and patient has increasingly been emphasized, where risk characteristics and time-related aspects should be taken into account to reach a decision based upon mutual agreement. The framework of time and risk for analysis can perform a useful role in clinical judgments and decisions, where framing of different features of risk might diminish discounting and increase compliance to treatment. A summary of valuation factors in medical decision making is presented: (a) long-term decisions are sensitive to discount rates; (b) discount rates vary by domain, by outcome, by individuals and by level of certainty; (c) probability discounting is used if the risk is perceived as controllable; (d) the doctor uses expected value, the patient is risk aversive; (e) asymmetric discounting for patients and doctors gives poor compliance; (f) discount rates are influenced by framing.

  13. Constructing diagnostic likelihood: clinical decisions using subjective versus statistical probability.

    Science.gov (United States)

    Kinnear, John; Jackson, Ruth

    2017-07-01

    Although physicians are highly trained in the application of evidence-based medicine, and are assumed to make rational decisions, there is evidence that their decision making is prone to biases. One of the biases that has been shown to affect accuracy of judgements is that of representativeness and base-rate neglect, where the saliency of a person's features leads to overestimation of their likelihood of belonging to a group. This results in the substitution of 'subjective' probability for statistical probability. This study examines clinicians' propensity to make estimations of subjective probability when presented with clinical information that is considered typical of a medical condition. The strength of the representativeness bias is tested by presenting choices in textual and graphic form. Understanding of statistical probability is also tested by omitting all clinical information. For the questions that included clinical information, 46.7% and 45.5% of clinicians made judgements of statistical probability, respectively. Where the question omitted clinical information, 79.9% of clinicians made a judgement consistent with statistical probability. There was a statistically significant difference in responses to the questions with and without representativeness information (χ2 (1, n=254)=54.45, pprobability. One of the causes for this representativeness bias may be the way clinical medicine is taught where stereotypic presentations are emphasised in diagnostic decision making. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  14. Diagnostic functional MRI: illustrated clinical applications and decision-making.

    Science.gov (United States)

    Bartsch, Andreas Joachim; Homola, György; Biller, Armin; Solymosi, László; Bendszus, Martin

    2006-06-01

    Functional magnetic resonance imaging (fMRI) has become a popular research tool, yet its use for diagnostic purposes and actual treatment planning has remained less widespread. The literature yields rather sparse evidence-based data on clinical fMRI applications and accordant decision-making. Notwithstanding, blood oxygenation level dependent (BOLD)- and arterial spin labeling (ASL)-fMRI can be judiciously combined with perfusion measurements, electroencephalographic (EEG) recordings, diffusion-weighted imaging (DWI), and fiber tractographies to assist clinical decisions. In this article we provide an overview of clinical fMRI applications based on illustrative examples. Assessment of cochlear implant candidates by fMRI is covered in some detail, and distinct reference is made to particular challenges imposed by brain tumors, other space-occupying lesions, cortical dysplasias, seizure disorders, and vascular malformations. Specific strategies, merits, and pitfalls of analyzing and interpreting diagnostic fMRI studies in individual patients are highlighted. Copyright 2006 Wiley-Liss, Inc.

  15. Towards Automation 2.0: A Neurocognitive Model for Environment Recognition, Decision-Making, and Action Execution

    Directory of Open Access Journals (Sweden)

    Zucker Gerhard

    2011-01-01

    Full Text Available The ongoing penetration of building automation by information technology is by far not saturated. Today's systems need not only be reliable and fault tolerant, they also have to regard energy efficiency and flexibility in the overall consumption. Meeting the quality and comfort goals in building automation while at the same time optimizing towards energy, carbon footprint and cost-efficiency requires systems that are able to handle large amounts of information and negotiate system behaviour that resolves conflicting demands—a decision-making process. In the last years, research has started to focus on bionic principles for designing new concepts in this area. The information processing principles of the human mind have turned out to be of particular interest as the mind is capable of processing huge amounts of sensory data and taking adequate decisions for (re-actions based on these analysed data. In this paper, we discuss how a bionic approach can solve the upcoming problems of energy optimal systems. A recently developed model for environment recognition and decision-making processes, which is based on research findings from different disciplines of brain research is introduced. This model is the foundation for applications in intelligent building automation that have to deal with information from home and office environments. All of these applications have in common that they consist of a combination of communicating nodes and have many, partly contradicting goals.

  16. An Automated Approach for Ranking Journals to Help in Clinician Decision Support

    Science.gov (United States)

    Jonnalagadda, Siddhartha R.; Moosavinasab, Soheil; Nath, Chinmoy; Li, Dingcheng; Chute, Christopher G.; Liu, Hongfang

    2014-01-01

    Point of care access to knowledge from full text journal articles supports decision-making and decreases medical errors. However, it is an overwhelming task to search through full text journal articles and find quality information needed by clinicians. We developed a method to rate journals for a given clinical topic, Congestive Heart Failure (CHF). Our method enables filtering of journals and ranking of journal articles based on source journal in relation to CHF. We also obtained a journal priority score, which automatically rates any journal based on its importance to CHF. Comparing our ranking with data gathered by surveying 169 cardiologists, who publish on CHF, our best Multiple Linear Regression model showed a correlation of 0.880, based on five-fold cross validation. Our ranking system can be extended to other clinical topics. PMID:25954382

  17. The role of clinical decision support in pharmacist response to drug-interaction alerts.

    Science.gov (United States)

    Miller, Luke; Steinmetz Pater, Karen; Corman, Shelby

    2015-01-01

    With over 100,000 different types of drug-drug interactions health care professionals rely heavily on automated drug-interaction alerts. Substantial variance in drug-interaction alerts yields opportunities for the use of clinical decision support (CDS) as a potential benefit to pharmacists. The purpose of this research was to determine whether decision support during dispensing impacts pharmacist response to drug-interaction alerts. A brief survey was administered to pharmacists in the community consisting of three patient cases, each containing three drug-drug interactions of varying severity. For each interaction, pharmacists were asked how they would respond, one group of pharmacists was randomly assigned to receive CDS while the other group did not. There were no significant differences in pharmacist response to alerts between the two groups. The control group did appear to be more likely to consult a drug reference, but this difference was not significant. While this study did not demonstrate a significant difference, drug-interaction alerts are still an area where improvements could be made. Advancements have the potential to reduce risk to patients and limit unnecessary hospital admissions. This study suggests that this level of clinical decision support has limited impact, but may prove beneficial by reducing the need to consult additional references. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Real-Time Clinical Decision Support Decreases Inappropriate Plasma Transfusion.

    Science.gov (United States)

    Shah, Neil; Baker, Steven A; Spain, David; Shieh, Lisa; Shepard, John; Hadhazy, Eric; Maggio, Paul; Goodnough, Lawrence T

    2017-08-01

    To curtail inappropriate plasma transfusions, we instituted clinical decision support as an alert upon order entry if the patient's recent international normalized ratio (INR) was 1.7 or less. The alert was suppressed for massive transfusion and within operative or apheresis settings. The plasma order was automatically removed upon alert acceptance while clinical exception reasons allowed for continued transfusion. Alert impact was studied comparing a 7-month control period with a 4-month intervention period. Monthly plasma utilization decreased 17.4%, from a mean ± SD of 3.40 ± 0.48 to 2.82 ± 0.6 plasma units per hundred patient days (95% confidence interval [CI] of difference, -0.1 to 1.3). Plasma transfused below an INR of 1.7 or less decreased from 47.6% to 41.6% (P = .0002; odds ratio, 0.78; 95% CI, 0.69-0.89). The alert recommendation was accepted 33% of the time while clinical exceptions were chosen in the remaining cases (active bleeding, 31%; other clinical indication, 33%; and apheresis, 2%). Alert acceptance rate varied significantly among different provider specialties. Clinical decision support can help curtail inappropriate plasma use but needs to be part of a comprehensive strategy including audit and feedback for comprehensive, long-term changes. © American Society for Clinical Pathology, 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

  19. Intuition in Clinical Decision Making: Differences Among Practicing Nurses.

    Science.gov (United States)

    Miller, Elizabeth M; Hill, Pamela D

    2017-08-01

    To examine the relationships and differences in the use of intuition among three categories of practicing nurses from various clinical units at a medical center in the Midwest. Descriptive, correlational, cross-sectional, prospective design. Three categories of nurses were based on the clinical unit: medical/surgical nurses ( n = 42), step-down/progressive care nurses ( n = 32), and critical care nurses ( n = 24). Participants were e-mailed the Rew Intuitive Judgment Scale (RIJS) via their employee e-mail to measure intuition in clinical practice. Participants were also asked to rate themselves according to Benner's (novice to expert) proficiency levels. Nurses practicing at higher self-reported proficiency levels, as defined by Benner, scored higher on the RIJS. More years of clinical experience were associated with higher self-reported levels of nursing proficiency and higher scores on the RIJS. There were no differences in intuition scores among the three categories of nurses. Nurses have many options, such as the nursing process, evidence-based clinical decision-making pathways, protocols, and intuition to aid them in the clinical decision-making process. Nurse educators and development professionals have a responsibility to recognize and embrace the multiple thought processes used by the nurse to better the nursing profession and positively affect patient outcomes.

  20. IBM’s Health Analytics and Clinical Decision Support

    Science.gov (United States)

    Sun, J.; Knoop, S.; Shabo, A.; Carmeli, B.; Sow, D.; Syed-Mahmood, T.; Rapp, W.

    2014-01-01

    Summary Objectives This survey explores the role of big data and health analytics developed by IBM in supporting the transformation of healthcare by augmenting evidence-based decision-making. Methods Some problems in healthcare and strategies for change are described. It is argued that change requires better decisions, which, in turn, require better use of the many kinds of healthcare information. Analytic resources that address each of the information challenges are described. Examples of the role of each of the resources are given. Results There are powerful analytic tools that utilize the various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions. There are early suggestions that these resources have clinical value. As with all analytic tools, they are limited by the amount and quality of data. Conclusion Big data is an inevitable part of the future of healthcare. There is a compelling need to manage and use big data to make better decisions to support the transformation of healthcare to the personalized, evidence-supported model of the future. Cognitive computing resources are necessary to manage the challenges in employing big data in healthcare. Such tools have been and are being developed. The analytic resources, themselves, do not drive, but support healthcare transformation. PMID:25123736

  1. Modeling information flows in clinical decision support: key insights for enhancing system effectiveness

    NARCIS (Netherlands)

    Medlock, Stephanie; Wyatt, Jeremy C.; Patel, Vimla L.; Shortliffe, Edward H.; Abu-Hanna, Ameen

    2016-01-01

    A fundamental challenge in the field of clinical decision support is to determine what characteristics of systems make them effective in supporting particular types of clinical decisions. However, we lack such a theory of decision support itself and a model to describe clinical decisions and the

  2. Clinical Decision Making of Nurses Working in Hospital Settings

    Directory of Open Access Journals (Sweden)

    Ida Torunn Bjørk

    2011-01-01

    Full Text Available This study analyzed nurses' perceptions of clinical decision making (CDM in their clinical practice and compared differences in decision making related to nurse demographic and contextual variables. A cross-sectional survey was carried out with 2095 nurses in four hospitals in Norway. A 24-item Nursing Decision Making Instrument based on cognitive continuum theory was used to explore how nurses perceived their CDM when meeting an elective patient for the first time. Data were analyzed with descriptive frequencies, t-tests, Chi-Square test, and linear regression. Nurses' decision making was categorized into analytic-systematic, intuitive-interpretive, and quasi-rational models of CDM. Most nurses reported the use of quasi-rational models during CDM thereby supporting the tenet that cognition most often includes properties of both analysis and intuition. Increased use of intuitive-interpretive models of CDM was associated with years in present job, further education, male gender, higher age, and working in predominantly surgical units.

  3. A study to explore if dentists’ anxiety affects their clinical decision-making

    OpenAIRE

    Chipchase, Susan Y.; Chapman, Helen R.; Bretherton, Roger

    2017-01-01

    Aims To develop a measure of dentists’ anxiety in clinical situations; to establish if dentists’ anxiety in clinical situations affected their self-reported clinical decision-making; to establish if occupational stress, as demonstrated by burnout, is associated with anxiety in clinical situations and clinical decision-making; and to explore the relationship between decision-making style and the clinical decisions which are influenced by anxiety. Design Cross-sectional study. Setting Primary D...

  4. WaterlooClarke: TREC 2015 Clinical Decision Support Track

    Science.gov (United States)

    2015-11-20

    infertility treatment and ectopic pregnancy Processed example (filtering + one synonyms+boolean operators): (dysmenorrhea OR men- orrhalgia) AND... medical questions (diagnosis, test and treatment articles). The two different full-text search engines we adopted in order to search over the collection of...Abstract Clinical decision support systems help physicians with finding additional information about a partic- ular medical case. In this paper, we

  5. Clinical decision-making of rural novice nurses.

    Science.gov (United States)

    Seright, T J

    2011-01-01

    Nurses in rural settings are often the first to assess and interpret the patient's clinical presentations. Therefore, an understanding of how nurses experience decision-making is important in terms of educational preparation, resource allocation to rural areas, institutional cultures, and patient outcomes. Theory development was based on the in-depth investigation of 12 novice nurses practicing in rural critical access hospitals in a north central state. This grounded theory study consisted of face-to-face interviews with 12 registered nurses, nine of whom were observed during their work day. The participants were interviewed a second time, as a method of member checking, and during this interview they reviewed their transcripts, the emerging themes and categories. Directors of nursing from both the research sites and rural hospitals not involved in the study, experienced researchers, and nurse educators facilitated triangulation of the findings. 'Sociocentric rationalizing' emerged as the central phenomenon and referred to the sense of belonging and agency which impacted the decision-making in this small group of novice nurses in rural critical access hospitals. The observed consequences, which were conceptualized during the axial coding process and were derived from observations and interviews of the 12 novice nurses in this study include: (1) gathering information before making a decision included assessment of: the credibility of co-workers, patients' subjective and objective data, and one's own past and current experiences; (2) conferring with co-workers as a direct method of confirming/denying decisions being made was considered more realistic and expedient than policy books and decision trees; (3) rural practicum clinical experiences, along with support after orientation, provide for transition to the rural nurse role; (4) involved directors of nursing served as both models and protectors of novice nurses placed in high accountability positions early in

  6. Medical students, clinical preventive services, and shared decision-making.

    Science.gov (United States)

    Keefe, Carole W; Thompson, Margaret E; Noel, Mary Margaret

    2002-11-01

    Improving access to preventive care requires addressing patient, provider, and systems barriers. Patients often lack knowledge or are skeptical about the importance of prevention. Physicians feel that they have too little time, are not trained to deliver preventive services, and are concerned about the effectiveness of prevention. We have implemented an educational module in the required family practice clerkship (1) to enhance medical student learning about common clinical preventive services and (2) to teach students how to inform and involve patients in shared decision making about those services. Students are asked to examine available evidence-based information for preventive screening services. They are encouraged to look at the recommendations of various organizations and use such resources as reports from the U.S. Preventive Services Task Force to determine recommendations they want to be knowledgeable about in talking with their patients. For learning shared decision making, students are trained to use a model adapted from Braddock and colleagues(1) to discuss specific screening services and to engage patients in the process of making informed decisions about what is best for their own health. The shared decision making is presented and modeled by faculty, discussed in small groups, and students practice using Web-based cases and simulations. The students are evaluated using formative and summative performance-based assessments as they interact with simulated patients about (1) screening for high blood cholesterol and other lipid abnormalities, (2) screening for colorectal cancer, (3) screening for prostate cancer, and (4) screening for breast cancer. The final student evaluation is a ten-minute, videotaped discussion with a simulated patient about screening for colorectal cancer that is graded against a checklist that focuses primarily on the elements of shared decision making. Our medical students appear quite willing to accept shared decision making as

  7. Clinical and genetic correlates of decision making in anorexia nervosa.

    Science.gov (United States)

    Tenconi, Elena; Degortes, Daniela; Clementi, Maurizio; Collantoni, Enrico; Pinato, Claudia; Forzan, Monica; Cassina, Matteo; Santonastaso, Paolo; Favaro, Angela

    2016-01-01

    Decision-making (DM) abilities have been found to be impaired in anorexia nervosa (AN), but few data are available about the characteristics and correlates of this cognitive function. The aim of the present study was to provide data on DM functioning in AN using both veridical and adaptive paradigms. While in veridical DM tasks, the individual's ability to predict a true/false response is measured, adaptive DM is the ability to consider both internal and external demands in order to make a good choice, in the absence of a single true "correct" answer. The participants were 189 women, of whom 91 were eating-disordered patients with a lifetime diagnosis of anorexia nervosa, and 98 were healthy women. All the participants underwent clinical, neuropsychological, and genetic assessment. The cognitive evaluation included a set of neuropsychological tasks and two decision-making tests: The Iowa Gambling Task and the Cognitive Bias Task. Anorexia nervosa patients showed significantly poorer performances on both decision-making tasks than healthy women. The Cognitive Bias Task revealed that anorexia nervosa patients employed significantly more context-independent decision-making strategies, which were independent from diagnostic subtype, handedness, education, and psychopathology. In the whole sample (patients and controls), Cognitive Bias Task performance was independently predicted by lifetime anorexia nervosa diagnosis, body mass index at assessment, and 5-HTTLPR genotype. Patients displayed poor decision-making functioning in both veridical and adaptive situations. The difficulties detected in anorexia nervosa individuals may affect not only the ability to consider the future outcomes of their actions (leading to "myopia for the future"), but also the capacity to update and review one's own mindset according to new environmental stimuli.

  8. Agile Acceptance Test-Driven Development of Clinical Decision Support Advisories: Feasibility of Using Open Source Software.

    Science.gov (United States)

    Basit, Mujeeb A; Baldwin, Krystal L; Kannan, Vaishnavi; Flahaven, Emily L; Parks, Cassandra J; Ott, Jason M; Willett, Duwayne L

    2018-04-13

    Moving to electronic health records (EHRs) confers substantial benefits but risks unintended consequences. Modern EHRs consist of complex software code with extensive local configurability options, which can introduce defects. Defects in clinical decision support (CDS) tools are surprisingly common. Feasible approaches to prevent and detect defects in EHR configuration, including CDS tools, are needed. In complex software systems, use of test-driven development and automated regression testing promotes reliability. Test-driven development encourages modular, testable design and expanding regression test coverage. Automated regression test suites improve software quality, providing a "safety net" for future software modifications. Each automated acceptance test serves multiple purposes, as requirements (prior to build), acceptance testing (on completion of build), regression testing (once live), and "living" design documentation. Rapid-cycle development or "agile" methods are being successfully applied to CDS development. The agile practice of automated test-driven development is not widely adopted, perhaps because most EHR software code is vendor-developed. However, key CDS advisory configuration design decisions and rules stored in the EHR may prove amenable to automated testing as "executable requirements." We aimed to establish feasibility of acceptance test-driven development of clinical decision support advisories in a commonly used EHR, using an open source automated acceptance testing framework (FitNesse). Acceptance tests were initially constructed as spreadsheet tables to facilitate clinical review. Each table specified one aspect of the CDS advisory's expected behavior. Table contents were then imported into a test suite in FitNesse, which queried the EHR database to automate testing. Tests and corresponding CDS configuration were migrated together from the development environment to production, with tests becoming part of the production regression test

  9. Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design.

    Science.gov (United States)

    Islam, Roosan; Weir, Charlene R; Jones, Makoto; Del Fiol, Guilherme; Samore, Matthew H

    2015-11-30

    Clinical experts' cognitive mechanisms for managing complexity have implications for the design of future innovative healthcare systems. The purpose of the study is to examine the constituents of decision complexity and explore the cognitive strategies clinicians use to control and adapt to their information environment. We used Cognitive Task Analysis (CTA) methods to interview 10 Infectious Disease (ID) experts at the University of Utah and Salt Lake City Veterans Administration Medical Center. Participants were asked to recall a complex, critical and vivid antibiotic-prescribing incident using the Critical Decision Method (CDM), a type of Cognitive Task Analysis (CTA). Using the four iterations of the Critical Decision Method, questions were posed to fully explore the incident, focusing in depth on the clinical components underlying the complexity. Probes were included to assess cognitive and decision strategies used by participants. The following three themes emerged as the constituents of decision complexity experienced by the Infectious Diseases experts: 1) the overall clinical picture does not match the pattern, 2) a lack of comprehension of the situation and 3) dealing with social and emotional pressures such as fear and anxiety. All these factors contribute to decision complexity. These factors almost always occurred together, creating unexpected events and uncertainty in clinical reasoning. Five themes emerged in the analyses of how experts deal with the complexity. Expert clinicians frequently used 1) watchful waiting instead of over- prescribing antibiotics, engaged in 2) theory of mind to project and simulate other practitioners' perspectives, reduced very complex cases into simple 3) heuristics, employed 4) anticipatory thinking to plan and re-plan events and consulted with peers to share knowledge, solicit opinions and 5) seek help on patient cases. The cognitive strategies to deal with decision complexity found in this study have important

  10. Medication-related clinical decision support alert overrides in inpatients.

    Science.gov (United States)

    Nanji, Karen C; Seger, Diane L; Slight, Sarah P; Amato, Mary G; Beeler, Patrick E; Her, Qoua L; Dalleur, Olivia; Eguale, Tewodros; Wong, Adrian; Silvers, Elizabeth R; Swerdloff, Michael; Hussain, Salman T; Maniam, Nivethietha; Fiskio, Julie M; Dykes, Patricia C; Bates, David W

    2018-05-01

    To define the types and numbers of inpatient clinical decision support alerts, measure the frequency with which they are overridden, and describe providers' reasons for overriding them and the appropriateness of those reasons. We conducted a cross-sectional study of medication-related clinical decision support alerts over a 3-year period at a 793-bed tertiary-care teaching institution. We measured the rate of alert overrides, the rate of overrides by alert type, the reasons cited for overrides, and the appropriateness of those reasons. Overall, 73.3% of patient allergy, drug-drug interaction, and duplicate drug alerts were overridden, though the rate of overrides varied by alert type (P 75% of the time. The vast majority of duplicate drug, patient allergy, and formulary substitution alerts were appropriate, suggesting that these categories of alerts might be good targets for refinement to reduce alert fatigue. Almost three-quarters of alerts were overridden, and 40% of the overrides were not appropriate. Future research should optimize alert types and frequencies to increase their clinical relevance, reducing alert fatigue so that important alerts are not inappropriately overridden.

  11. Clinical decision support must be useful, functional is not enough

    DEFF Research Database (Denmark)

    Kortteisto, Tiina; Komulainen, Jorma; Mäkelä, Marjukka

    2012-01-01

    the use of computer-based clinical decision support (eCDS) in primary care and how different professional groups experience it. Our aim was to describe specific reasons for using or not using eCDS among primary care professionals. METHODS: The setting was a Finnish primary health care organization with 48...... professionals receiving patient-specific guidance at the point of care. Multiple data (focus groups, questionnaire and spontaneous feedback) were analyzed using deductive content analysis and descriptive statistics. RESULTS: The content of the guidance is a significant feature of the primary care professional...

  12. Guideline Formalization and Knowledge Representation for Clinical Decision Support

    Directory of Open Access Journals (Sweden)

    Paulo NOVAIS

    2013-07-01

    Full Text Available Normal 0 21 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} The prevalence of situations of medical error and defensive medicine in healthcare institutions is a great concern of the medical community. Clinical Practice Guidelines are regarded by most researchers as a way to mitigate these occurrences; however, there is a need to make them interactive, easier to update and to deploy. This paper provides a model for Computer-Interpretable Guidelines based on the generic tasks of the clinical process, devised to be included in the framework of a Clinical Decision Support System. Aiming to represent medical recommendations in a simple and intuitive way. Hence, this work proposes a knowledge representation formalism that uses an Extension to Logic Programming to handle incomplete information. This model is used to represent different cases of missing, conflicting and inexact information with the aid of a method to quantify its quality. The integration of the guideline model with the knowledge representation formalism yields a clinical decision model that relies on the development of multiple information scenarios and the exploration of different clinical hypotheses.

  13. Guideline Formalization and Knowledge Representation for Clinical Decision Support

    Directory of Open Access Journals (Sweden)

    Tiago OLIVEIRA

    2012-09-01

    Full Text Available Normal 0 21 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} The prevalence of situations of medical error and defensive medicine in healthcare institutions is a great concern of the medical community. Clinical Practice Guidelines are regarded by most researchers as a way to mitigate theseoccurrences; however, there is a need to make them interactive, easier to update and to deploy. This paper provides a model for Computer-Interpretable Guidelines based on the generic tasks of the clinical process, devised to be included in the framework of a Clinical Decision Support System. Aiming to represent medical recommendations in a simple and intuitive way. Hence, this work proposes a knowledge representation formalism that uses an Extension to Logic Programming to handle incomplete information. This model is used to represent different cases of missing, conflicting and inexact information with the aid of a method to quantify its quality. The integration of the guideline model with the knowledge representation formalism yields a clinical decision model that relies on the development of multiple information scenarios and the exploration of different clinical hypotheses.

  14. Clinical decision-making and secondary findings in systems medicine.

    Science.gov (United States)

    Fischer, T; Brothers, K B; Erdmann, P; Langanke, M

    2016-05-21

    Systems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology (especially systems biology); "big data" statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate systems medicine knowledge and clinicians working to apply it. This article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through "big data" approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the "death computer," to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their

  15. Detection of tuberculosis using digital chest radiography: automated reading vs. interpretation by clinical officers.

    Science.gov (United States)

    Maduskar, P; Muyoyeta, M; Ayles, H; Hogeweg, L; Peters-Bax, L; van Ginneken, B

    2013-12-01

    A busy urban health centre in Lusaka, Zambia. To compare the accuracy of automated reading (CAD4TB) with the interpretation of digital chest radiograph (CXR) by clinical officers for the detection of tuberculosis (TB). A retrospective analysis was performed on 161 subjects enrolled in a TB specimen bank study. CXRs were analysed using CAD4TB, which computed an image abnormality score (0-100). Four clinical officers scored the CXRs for abnormalities consistent with TB. We compared the automated readings and the readings by clinical officers against the bacteriological and radiological results used as reference. We report here the area under the receiver operating characteristic curve (AUC) and kappa (κ) statistics. Of 161 enrolled subjects, 97 had bacteriologically confirmed TB and 120 had abnormal CXR. The AUCs for CAD4TB and the clinical officers were respectively 0.73 and 0.65-0.75 in comparison with the bacteriological reference, and 0.91 and 0.89-0.94 in comparison with the radiological reference. P values indicated no significant differences, except for one clinical officer who performed significantly worse than CAD4TB (P < 0.05) using the bacteriological reference. κ values for CAD4TB and clinical officers with radiological reference were respectively 0.61 and 0.49-0.67. CXR assessment using CAD4TB and by clinical officers is comparable. CAD4TB has potential as a point-of-care test and for the automated identification of subjects who require further examinations.

  16. Monitoring, accounting and automated decision support for the ALICE experiment based on the MonALISA framework

    CERN Document Server

    Cirstoiu, C; Betev, L; Saiz, P; Peters, A J; Muraru, A; Voicu, R; Legrand, I

    2007-01-01

    We are developing a general purpose monitoring system for the ALICE experiment, based on the MonALISA framework. MonALISA (Monitoring Agents using a Large Integrated Services Architecture) is a fully distributed system with no single point of failure that is able to collect, store monitoring information and present it as significant perspectives and synthetic views on the status and the trends of the entire system. Furthermore, agents can use it for taking automated operational decisions. Monitoring information is gathered locally from all the components running in each site. The entire flow of information is aggregated on site level by a MonALISA service and then collected and presented in various forms by a central MonALISA Repository. Based on this information, other services take operational decisions such as alerts, triggers, service restarts and automatic production job or transfer submissions. The system monitors all the components: computer clusters (all major parameters of each computing node), jobs ...

  17. Clinical Utility of an Automated Instrument for Gram Staining Single Slides ▿

    Science.gov (United States)

    Baron, Ellen Jo; Mix, Samantha; Moradi, Wais

    2010-01-01

    Gram stains of 87 different clinical samples were prepared by the laboratory's conventional methods (automated or manual) and by a new single-slide-type automated staining instrument, GG&B AGS-1000. Gram stains from either heat- or methanol-fixed slides stained with the new instrument were easy to interpret, and results were essentially the same as those from the methanol-fixed slides prepared as a part of the routine workflow. This instrument is well suited to a rapid-response laboratory where Gram stain requests are commonly received on a stat basis. PMID:20410348

  18. Clinical utility of an automated instrument for gram staining single slides.

    Science.gov (United States)

    Baron, Ellen Jo; Mix, Samantha; Moradi, Wais

    2010-06-01

    Gram stains of 87 different clinical samples were prepared by the laboratory's conventional methods (automated or manual) and by a new single-slide-type automated staining instrument, GG&B AGS-1000. Gram stains from either heat- or methanol-fixed slides stained with the new instrument were easy to interpret, and results were essentially the same as those from the methanol-fixed slides prepared as a part of the routine workflow. This instrument is well suited to a rapid-response laboratory where Gram stain requests are commonly received on a stat basis.

  19. Clinical Decision Support Knowledge Management: Strategies for Success.

    Science.gov (United States)

    Khalifa, Mohamed; Alswailem, Osama

    2015-01-01

    Clinical Decision Support Systems have been shown to increase quality of care, patient safety, improve adherence to guidelines for prevention and treatment, and avoid medication errors. Such systems depend mainly on two types of content; the clinical information related to patients and the medical knowledge related to the specialty that informs the system rules and alerts. At King Faisal Specialist Hospital and Research Center, Saudi Arabia, the Health Information Technology Affairs worked on identifying best strategies and recommendations for successful CDSS knowledge management. A review of literature was conducted to identify main areas of challenges and factors of success. A qualitative survey was used over six months' duration to collect opinions, experiences and suggestions from both IT and healthcare professionals. Recommendations were categorized into ten main topics that should be addressed during the development and implementation of CDSS knowledge management tools in the hospital.

  20. Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach.

    Science.gov (United States)

    Bennett, Casey C; Hauser, Kris

    2013-01-01

    In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and (2) the basis for clinical artificial intelligence - an AI that can "think like a doctor". This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans as actions are performed and new observations are obtained. This framework was evaluated using real patient data from an electronic health record. The results demonstrate the feasibility of this approach; such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare. The cost per unit of outcome change (CPUC) was $189 vs. $497 for AI vs. TAU (where lower is considered optimal) - while at the same time the AI approach could obtain a 30-35% increase in patient outcomes. Tweaking certain AI model parameters could further enhance this advantage, obtaining approximately 50% more improvement (outcome change) for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal

  1. An automated knowledge-based textual summarization system for longitudinal, multivariate clinical data.

    Science.gov (United States)

    Goldstein, Ayelet; Shahar, Yuval

    2016-06-01

    Design and implement an intelligent free-text summarization system: The system's input includes large numbers of longitudinal, multivariate, numeric and symbolic clinical raw data, collected over varying periods of time, and in different complex contexts, and a suitable medical knowledge base. The system then automatically generates a textual summary of the data. We aim to prove the feasibility of implementing such a system, and to demonstrate its potential benefits for clinicians and for enhancement of quality of care. We have designed a new, domain-independent, knowledge-based system, the CliniText system, for automated summarization in free text of longitudinal medical records of any duration, in any context. The system is composed of six components: (1) A temporal abstraction module generates all possible abstractions from the patient's raw data using a temporal-abstraction knowledge base; (2) The abductive reasoning module infers abstractions or events from the data, which were not explicitly included in the database; (3) The pruning module filters out raw or abstract data based on predefined heuristics; (4) The document structuring module organizes the remaining raw or abstract data, according to the desired format; (5) The microplanning module, groups the raw or abstract data and creates referring expressions; (6) The surface realization module, generates the text, and applies the grammar rules of the chosen language. We have performed an initial technical evaluation of the system in the cardiac intensive-care and diabetes domains. We also summarize the results of a more detailed evaluation study that we have performed in the intensive-care domain that assessed the completeness, correctness, and overall quality of the system's generated text, and its potential benefits to clinical decision making. We assessed these measures for 31 letters originally composed by clinicians, and for the same letters when generated by the CliniText system. We have successfully

  2. The Value of Information in Automated Negotiation: A Decision Model for Eliciting User Preferences

    NARCIS (Netherlands)

    T. Baarslag (Tim); M. Kaisers (Michael)

    2017-01-01

    textabstractConsider an agent that can autonomously negotiate and coordinate with others in our stead, to reach outcomes and agreements in our interest. Such automated negotiation agents are already common practice in areas such as high frequency trading, and are now finding applications in domains

  3. Automated validation of patient safety clinical incident classification: macro analysis.

    Science.gov (United States)

    Gupta, Jaiprakash; Patrick, Jon

    2013-01-01

    Patient safety is the buzz word in healthcare. Incident Information Management System (IIMS) is electronic software that stores clinical mishaps narratives in places where patients are treated. It is estimated that in one state alone over one million electronic text documents are available in IIMS. In this paper we investigate the data density available in the fields entered to notify an incident and the validity of the built in classification used by clinician to categories the incidents. Waikato Environment for Knowledge Analysis (WEKA) software was used to test the classes. Four statistical classifier based on J48, Naïve Bayes (NB), Naïve Bayes Multinominal (NBM) and Support Vector Machine using radial basis function (SVM_RBF) algorithms were used to validate the classes. The data pool was 10,000 clinical incidents drawn from 7 hospitals in one state in Australia. In first part of the study 1000 clinical incidents were selected to determine type and number of fields worth investigating and in the second part another 5448 clinical incidents were randomly selected to validate 13 clinical incident types. Result shows 74.6% of the cells were empty and only 23 fields had content over 70% of the time. The percentage correctly classified classes on four algorithms using categorical dataset ranged from 42 to 49%, using free-text datasets from 65% to 77% and using both datasets from 72% to 79%. Kappa statistic ranged from 0.36 to 0.4. for categorical data, from 0.61 to 0.74. for free-text and from 0.67 to 0.77 for both datasets. Similar increases in performance in the 3 experiments was noted on true positive rate, precision, F-measure and area under curve (AUC) of receiver operating characteristics (ROC) scores. The study demonstrates only 14 of 73 fields in IIMS have data that is usable for machine learning experiments. Irrespective of the type of algorithms used when all datasets are used performance was better. Classifier NBM showed best performance. We think the

  4. Clinical genomics information management software linking cancer genome sequence and clinical decisions.

    Science.gov (United States)

    Watt, Stuart; Jiao, Wei; Brown, Andrew M K; Petrocelli, Teresa; Tran, Ben; Zhang, Tong; McPherson, John D; Kamel-Reid, Suzanne; Bedard, Philippe L; Onetto, Nicole; Hudson, Thomas J; Dancey, Janet; Siu, Lillian L; Stein, Lincoln; Ferretti, Vincent

    2013-09-01

    Using sequencing information to guide clinical decision-making requires coordination of a diverse set of people and activities. In clinical genomics, the process typically includes sample acquisition, template preparation, genome data generation, analysis to identify and confirm variant alleles, interpretation of clinical significance, and reporting to clinicians. We describe a software application developed within a clinical genomics study, to support this entire process. The software application tracks patients, samples, genomic results, decisions and reports across the cohort, monitors progress and sends reminders, and works alongside an electronic data capture system for the trial's clinical and genomic data. It incorporates systems to read, store, analyze and consolidate sequencing results from multiple technologies, and provides a curated knowledge base of tumor mutation frequency (from the COSMIC database) annotated with clinical significance and drug sensitivity to generate reports for clinicians. By supporting the entire process, the application provides deep support for clinical decision making, enabling the generation of relevant guidance in reports for verification by an expert panel prior to forwarding to the treating physician. Copyright © 2013 Elsevier Inc. All rights reserved.

  5. A bench-top automated workstation for nucleic acid isolation from clinical sample types.

    Science.gov (United States)

    Thakore, Nitu; Garber, Steve; Bueno, Arial; Qu, Peter; Norville, Ryan; Villanueva, Michael; Chandler, Darrell P; Holmberg, Rebecca; Cooney, Christopher G

    2018-04-18

    Systems that automate extraction of nucleic acid from cells or viruses in complex clinical matrices have tremendous value even in the absence of an integrated downstream detector. We describe our bench-top automated workstation that integrates our previously-reported extraction method - TruTip - with our newly-developed mechanical lysis method. This is the first report of this method for homogenizing viscous and heterogeneous samples and lysing difficult-to-disrupt cells using "MagVor": a rotating magnet that rotates a miniature stir disk amidst glass beads confined inside of a disposable tube. Using this system, we demonstrate automated nucleic acid extraction from methicillin-resistant Staphylococcus aureus (MRSA) in nasopharyngeal aspirate (NPA), influenza A in nasopharyngeal swabs (NPS), human genomic DNA from whole blood, and Mycobacterium tuberculosis in NPA. The automated workstation yields nucleic acid with comparable extraction efficiency to manual protocols, which include commercially-available Qiagen spin column kits, across each of these sample types. This work expands the scope of applications beyond previous reports of TruTip to include difficult-to-disrupt cell types and automates the process, including a method for removal of organics, inside a compact bench-top workstation. Copyright © 2018 Elsevier B.V. All rights reserved.

  6. Endodontic retreatment. Aspects of decision making and clinical outcome.

    Science.gov (United States)

    Kvist, T

    2001-01-01

    regardless of assessment method. Compared with Standard gamble Visual Analogue Scale systematically produced lower ratings. U-values were found to change considerably in both the short and long-term. Any significant correlation between endodontists' U-values and retreatment prescriptions could not be demonstrated. Surgical and nonsurgical retreatment were randomly assigned to 95 "failed" root filled teeth in 92 patients. Cases were followed clinically and radiographically for four years postoperatively. At the 12-month recall a statistically significant higher healing rate was observed for teeth retreated surgically. At the final 48-month recall no systematic difference was detected. Patients were found to be more subject to postoperative discomfort when teeth were retreated surgically compared with nonsurgically. Consequently, surgical retreatment tended to be associated with higher indirect costs than a nonsurgically approach. In the final part of the thesis it is argued that retreatment decision making in everyday clinical practice normally should be based on simple principles. It is suggested that in order to achieve the best overall consequence a periapical lesion in a root filled tooth that is not expected to heal should be retreated. Arguments to withhold retreatment should be based on (i) respect for patient autonomy, (ii) retreatment risks or (iii) retreatment costs.

  7. Transforming clinical practice guidelines and clinical pathways into fast-and-frugal decision trees to improve clinical care strategies.

    Science.gov (United States)

    Djulbegovic, Benjamin; Hozo, Iztok; Dale, William

    2018-02-27

    Contemporary delivery of health care is inappropriate in many ways, largely due to suboptimal Q5 decision-making. A typical approach to improve practitioners' decision-making is to develop evidence-based clinical practice guidelines (CPG) by guidelines panels, who are instructed to use their judgments to derive practice recommendations. However, mechanisms for the formulation of guideline judgments remains a "black-box" operation-a process with defined inputs and outputs but without sufficient knowledge of its internal workings. Increased explicitness and transparency in the process can be achieved by implementing CPG as clinical pathways (CPs) (also known as clinical algorithms or flow-charts). However, clinical recommendations thus derived are typically ad hoc and developed by experts in a theory-free environment. As any recommendation can be right (true positive or negative), or wrong (false positive or negative), the lack of theoretical structure precludes the quantitative assessment of the management strategies recommended by CPGs/CPs. To realize the full potential of CPGs/CPs, they need to be placed on more solid theoretical grounds. We believe this potential can be best realized by converting CPGs/CPs within the heuristic theory of decision-making, often implemented as fast-and-frugal (FFT) decision trees. This is possible because FFT heuristic strategy of decision-making can be linked to signal detection theory, evidence accumulation theory, and a threshold model of decision-making, which, in turn, allows quantitative analysis of the accuracy of clinical management strategies. Fast-and-frugal provides a simple and transparent, yet solid and robust, methodological framework connecting decision science to clinical care, a sorely needed missing link between CPGs/CPs and patient outcomes. We therefore advocate that all guidelines panels express their recommendations as CPs, which in turn should be converted into FFTs to guide clinical care. © 2018 John Wiley

  8. Impact of a Clinical Decision Support System on Pharmacy Clinical Interventions, Documentation Efforts, and Costs

    OpenAIRE

    Calloway, Stacy; Akilo, Hameed A.; Bierman, Kyle

    2013-01-01

    Health care organizations are turning to electronic clinical decision support systems (CDSSs) to increase quality of patient care and promote a safer environment. A CDSS is a promising approach to the aggregation and use of patient data to identify patients who would most benefit from interventions by pharmacy clinicians. However, there are limited published reports describing the impact of CDSS on clinical pharmacy measures. In February 2011, Good Shepherd Medical Center, a 425-bed acute car...

  9. Automating clinical practice guidelines: a corporate-academic partnership.

    Science.gov (United States)

    Friedberg, R C; Moser, S A; Jamieson, P W; Margulies, D M; Smith, J A; McDonald, J M

    1996-01-01

    Implementation of guidelines offers one of the largest opportunities for quality improvement, utilization review, and cost control for the health-care enterprise. If guidelines could be implemented on a large scale, their adoption could result in $100 billion in annual savings as well as improve the quality of patient care. However, infrastructural barriers impede progress. Collaboration between the Laboratory Medicine Health Services Program at the University of Alabama at Birmingham, Columbia-Presbyterian Medical Center, and the Cerner Corporation, funded by the National Institute of Standards and Technology as part of the Advanced Technology Program involving ¿Information Infrastructure for Healthcare,¿ is focused on developing and delivering: 1) methods for creating operational forms of guidelines; 2) an effective computer-based architecture for implementing guidelines in clinical practice; 3) methods for packaging guidelines for wide distribution; 4) methods for testing the efficacy, safety, and acceptability of guidelines; and 5) a model for collecting, aggregating, and normalizing data from disparate systems. This hypothesis-driven research program is focused on laboratory medicine-based guidelines as a tool for developing, testing, and evaluating methods that can be implemented widely.

  10. Clinical implementation of RNA signatures for pharmacogenomic decision-making

    Directory of Open Access Journals (Sweden)

    Tang W

    2011-09-01

    Full Text Available Weihua Tang1, Zhiyuan Hu2, Hind Muallem1, Margaret L Gulley1,21Department of Pathology and Laboratory Medicine, 2Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, North Carolina, NC, USAAbstract: RNA profiling is increasingly used to predict drug response, dose, or toxicity based on analysis of drug pharmacokinetic or pharmacodynamic pathways. Before implementing multiplexed RNA arrays in clinical practice, validation studies are carried out to demonstrate sufficient evidence of analytic and clinical performance, and to establish an assay protocol with quality assurance measures. Pathologists assure quality by selecting input tissue and by interpreting results in the context of the input tissue as well as the technologies that were used and the clinical setting in which the test was ordered. A strength of RNA profiling is the array-based measurement of tens to thousands of RNAs at once, including redundant tests for critical analytes or pathways to promote confidence in test results. Instrument and reagent manufacturers are crucial for supplying reliable components of the test system. Strategies for quality assurance include careful attention to RNA preservation and quality checks at pertinent steps in the assay protocol, beginning with specimen collection and proceeding through the various phases of transport, processing, storage, analysis, interpretation, and reporting. Specimen quality is checked by probing housekeeping transcripts, while spiked and exogenous controls serve as a check on analytic performance of the test system. Software is required to manipulate abundant array data and present it for interpretation by a laboratory physician who reports results in a manner facilitating therapeutic decision-making. Maintenance of the assay requires periodic documentation of personnel competency and laboratory proficiency. These strategies are shepherding genomic arrays into clinical settings to provide added

  11. Peripheral Exophytic Oral Lesions: A Clinical Decision Tree

    Directory of Open Access Journals (Sweden)

    Hamed Mortazavi

    2017-01-01

    Full Text Available Diagnosis of peripheral oral exophytic lesions might be quite challenging. This review article aimed to introduce a decision tree for oral exophytic lesions according to their clinical features. General search engines and specialized databases including PubMed, PubMed Central, Medline Plus, EBSCO, Science Direct, Scopus, Embase, and authenticated textbooks were used to find relevant topics by means of keywords such as “oral soft tissue lesion,” “oral tumor like lesion,” “oral mucosal enlargement,” and “oral exophytic lesion.” Related English-language articles published since 1988 to 2016 in both medical and dental journals were appraised. Upon compilation of data, peripheral oral exophytic lesions were categorized into two major groups according to their surface texture: smooth (mesenchymal or nonsquamous epithelium-originated and rough (squamous epithelium-originated. Lesions with smooth surface were also categorized into three subgroups according to their general frequency: reactive hyperplastic lesions/inflammatory hyperplasia, salivary gland lesions (nonneoplastic and neoplastic, and mesenchymal lesions (benign and malignant neoplasms. In addition, lesions with rough surface were summarized in six more common lesions. In total, 29 entities were organized in the form of a decision tree in order to help clinicians establish a logical diagnosis by a stepwise progression method.

  12. Clinical decision support system for the diagnosis of adolescence health.

    Science.gov (United States)

    Moutsouri, Irene; Nikou, Amalia; Pampalou, Machi; Lentza, Maria; Spyridakis, Paulos; Mathiopoulou, Natassa; Konsoulas, Dimitris; Lampou, Marianna; Alexiou, Athanasios

    2015-01-01

    It is common that children confront psychological problems when they reach puberty. These problems could easily be overcome, but in many cases they could be severe, leading to social estrangement or worse in madness or death. According to information collected we designed a questionnaire about the psychology of adolescents in order to help people in that age or their elders find out if they have health issues. We used already published researches and material concerning all the psychological problems a child can confront in order to make a reliable questionnaire and to develop the clinical decision support system. Our main objective is to publish and administrate a web-based free tool for sharing medical knowledge about any psychological disease a child can already have or develop during puberty.

  13. Exploration Clinical Decision Support System: Medical Data Architecture

    Science.gov (United States)

    Lindsey, Tony; Shetye, Sandeep; Shaw, Tianna (Editor)

    2016-01-01

    The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear

  14. Robot decisions: on the importance of virtuous judgment in clinical decision making.

    Science.gov (United States)

    Gelhaus, Petra

    2011-10-01

    The aim of this article is to argue for the necessity of emotional professional virtues in the understanding of good clinical practice. This understanding is required for a proper balance of capacities in medical education and further education of physicians. For this reason an ideal physician, incarnating the required virtues, skills and knowledge is compared with a non-emotional robot that is bound to moral rules. This fictive confrontation is meant to clarify why certain demands on the personality of the physician are justified, in addition to a rule- and principle-based moral orientation and biomedical knowledge and skills. Philosophical analysis of thought experiments inspired by science fiction literature by Isaac Asimov. Although prima facie a rule-oriented robot seems more reliable and trustworthy, the complexity of clinical judgment is not met by an encompassing and never contradictory set of rules from which one could logically derive decisions. There are different ways how the robot could still work, but at the cost of the predictability of its behaviour and its moral orientation. In comparison, a virtuous human doctor who is also bound to these rules, although less strictly, will more reliably keep at moral objectives, be understandable, be more flexible in case the rules come to their limits, and will be more predictable in these critical situations. Apart from these advantages of the virtuous human doctor referring to her own person, the most problematic deficit of the robot is its lacking deeper understanding of the inner mental events of patients which makes good contact, good communication and good influence impossible. Although an infallibly rule-oriented robot seems more reliable at first view, in situations that require complex decisions like clinical practice the agency of a moral human person is more trustworthy. Furthermore, the understanding of the patient's emotions must remain insufficient for a non-emotional, non-human being. Because

  15. Automated Creation of Datamarts from a Clinical Data Warehouse, Driven by an Active Metadata Repository

    Science.gov (United States)

    Rogerson, Charles L.; Kohlmiller, Paul H.; Stutman, Harris

    1998-01-01

    A methodology and toolkit are described which enable the automated metadata-driven creation of datamarts from clinical data warehouses. The software uses schema-to-schema transformation driven by an active metadata repository. Tools for assessing datamart data quality are described, as well as methods for assessing the feasibility of implementing specific datamarts. A methodology for data remediation and the re-engineering of operational data capture is described.

  16. MODULAR ANALYTICS: A New Approach to Automation in the Clinical Laboratory

    OpenAIRE

    Horowitz, Gary L.; Zaman, Zahur; Blanckaert, Norbert J. C.; Chan, Daniel W.; Dubois, Jeffrey A.; Golaz, Olivier; Mensi, Noury; Keller, Franz; Stolz, Herbert; Klingler, Karl; Marocchi, Alessandro; Prencipe, Lorenzo; McLawhon, Ronald W.; Nilsen, Olaug L.; Oellerich, Michael

    2005-01-01

    MODULAR ANALYTICS (Roche Diagnostics) (MODULAR ANALYTICS, Elecsys and Cobas Integra are trademarks of a member of the Roche Group) represents a new approach to automation for the clinical chemistry laboratory. It consists of a control unit, a core unit with a bidirectional multitrack rack transportation system, and three distinct kinds of analytical modules: an ISE module, a P800 module (44 photometric tests, throughput of up to 800 tests/h), and a D2400 module (16 photometric tests, throughp...

  17. [Clinical application of automated digital image analysis for morphology review of peripheral blood leukocyte].

    Science.gov (United States)

    Xing, Ying; Yan, Xiaohua; Pu, Chengwei; Shang, Ke; Dong, Ning; Wang, Run; Wang, Jianzhong

    2016-03-01

    To explore the clinical application of automated digital image analysis in leukocyte morphology examination when review criteria of hematology analyzer are triggered. The reference range of leukocyte differentiation by automated digital image analysis was established by analyzing 304 healthy blood samples from Peking University First Hospital. Six hundred and ninty-seven blood samples from Peking University First Hospital were randomly collected from November 2013 to April 2014, complete blood cells were counted on hematology analyzer, blood smears were made and stained at the same time. Blood smears were detected by automated digital image analyzer and the results were checked (reclassification) by a staff with abundant morphology experience. The same smear was examined manually by microscope. The results by manual microscopic differentiation were used as"golden standard", and diagnostic efficiency of abnormal specimens by automated digital image analysis was calculated, including sensitivity, specificity and accuracy. The difference of abnormal leukocytes detected by two different methods was analyzed in 30 samples of hematological and infectious diseases. Specificity of identifying abnormalities of white blood cells by automated digital image analysis was more than 90% except monocyte. Sensitivity of neutrophil toxic abnormities (including Döhle body, toxic granulate and vacuolization) was 100%; sensitivity of blast cells, immature granulates and atypical lymphocytes were 91.7%, 60% to 81.5% and 61.5%, respectively. Sensitivity of leukocyte differential count was 91.8% for neutrophils, 88.5% for lymphocytes, 69.1% for monocytes, 78.9% for eosinophils and 36.3 for basophils. The positive rate of recognizing abnormal cells (blast, immature granulocyte and atypical lymphocyte) by manual microscopic method was 46.7%, 53.3% and 10%, respectively. The positive rate of automated digital image analysis was 43.3%, 60% and 10%, respectively. There was no statistic

  18. Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies.

    Science.gov (United States)

    Samwald, Matthias; Miñarro Giménez, Jose Antonio; Boyce, Richard D; Freimuth, Robert R; Adlassnig, Klaus-Peter; Dumontier, Michel

    2015-02-22

    Every year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics. We developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles. Our methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners. The ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of

  19. Profiling and Automated Decision Making in the Present and New EU Data Protection Frameworks

    DEFF Research Database (Denmark)

    Savin, Andrej

    made possible by a transfer of a staggering portion of our daily lives from the offline world to the Internet. It is a truism that automation would be impossible without our willing participation on the Internet. We freely take part in social networks, post on blogs, and send our emails. On the other......”.1 This is then harvested for connections and correlations and profiles created that can be used for commercial and other purposes. We fear this world but are also dependant on it. The creation of these profiles and their usage is an uncharted territory for the social sciences as much...

  20. A semi-automated tool for treatment plan-quality evaluation and clinical trial quality assurance

    International Nuclear Information System (INIS)

    Wang, Jiazhou; Chen, Wenzhou; Studenski, Matthew; Cui, Yunfeng; Xiao, Ying; Lee, Andrew J

    2013-01-01

    The goal of this work is to develop a plan-quality evaluation program for clinical routine and multi-institutional clinical trials so that the overall evaluation efficiency is improved. In multi-institutional clinical trials evaluating the plan quality is a time-consuming and labor-intensive process. In this note, we present a semi-automated plan-quality evaluation program which combines MIMVista, Java/MATLAB, and extensible markup language (XML). More specifically, MIMVista is used for data visualization; Java and its powerful function library are implemented for calculating dosimetry parameters; and to improve the clarity of the index definitions, XML is applied. The accuracy and the efficiency of the program were evaluated by comparing the results of the program with the manually recorded results in two RTOG trials. A slight difference of about 0.2% in volume or 0.6 Gy in dose between the semi-automated program and manual recording was observed. According to the criteria of indices, there are minimal differences between the two methods. The evaluation time is reduced from 10–20 min to 2 min by applying the semi-automated plan-quality evaluation program. (note)

  1. A semi-automated tool for treatment plan-quality evaluation and clinical trial quality assurance

    Science.gov (United States)

    Wang, Jiazhou; Chen, Wenzhou; Studenski, Matthew; Cui, Yunfeng; Lee, Andrew J.; Xiao, Ying

    2013-07-01

    The goal of this work is to develop a plan-quality evaluation program for clinical routine and multi-institutional clinical trials so that the overall evaluation efficiency is improved. In multi-institutional clinical trials evaluating the plan quality is a time-consuming and labor-intensive process. In this note, we present a semi-automated plan-quality evaluation program which combines MIMVista, Java/MATLAB, and extensible markup language (XML). More specifically, MIMVista is used for data visualization; Java and its powerful function library are implemented for calculating dosimetry parameters; and to improve the clarity of the index definitions, XML is applied. The accuracy and the efficiency of the program were evaluated by comparing the results of the program with the manually recorded results in two RTOG trials. A slight difference of about 0.2% in volume or 0.6 Gy in dose between the semi-automated program and manual recording was observed. According to the criteria of indices, there are minimal differences between the two methods. The evaluation time is reduced from 10-20 min to 2 min by applying the semi-automated plan-quality evaluation program.

  2. A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree.

    Science.gov (United States)

    Exarchos, Themis P; Tsipouras, Markos G; Exarchos, Costas P; Papaloukas, Costas; Fotiadis, Dimitrios I; Michalis, Lampros K

    2007-07-01

    In the current work we propose a methodology for the automated creation of fuzzy expert systems, applied in ischaemic and arrhythmic beat classification. The proposed methodology automatically creates a fuzzy expert system from an initial training dataset. The approach consists of three stages: (a) extraction of a crisp set of rules from a decision tree induced from the training dataset, (b) transformation of the crisp set of rules into a fuzzy model and (c) optimization of the fuzzy model's parameters using global optimization. The above methodology is employed in order to create fuzzy expert systems for ischaemic and arrhythmic beat classification in ECG recordings. The fuzzy expert system for ischaemic beat detection is evaluated in a cardiac beat dataset that was constructed using recordings from the European Society of Cardiology ST-T database. The arrhythmic beat classification fuzzy expert system is evaluated using the MIT-BIH arrhythmia database. The fuzzy expert system for ischaemic beat classification reported 91% sensitivity and 92% specificity. The arrhythmic beat classification fuzzy expert system reported 96% average sensitivity and 99% average specificity for all categories. The proposed methodology provides high accuracy and the ability to interpret the decisions made. The fuzzy expert systems for ischaemic and arrhythmic beat classification compare well with previously reported results, indicating that they could be part of an overall clinical system for ECG analysis and diagnosis.

  3. Development of a clinical decision model for thyroid nodules

    Directory of Open Access Journals (Sweden)

    Eberhardt John

    2009-08-01

    Full Text Available Abstract Background Thyroid nodules represent a common problem brought to medical attention. Four to seven percent of the United States adult population (10–18 million people has a palpable thyroid nodule, however the majority (>95% of thyroid nodules are benign. While, fine needle aspiration remains the most cost effective and accurate diagnostic tool for thyroid nodules in current practice, over 20% of patients undergoing FNA of a thyroid nodule have indeterminate cytology (follicular neoplasm with associated malignancy risk prevalence of 20–30%. These patients require thyroid lobectomy/isthmusectomy purely for the purpose of attaining a definitive diagnosis. Given that the majority (70–80% of these patients have benign surgical pathology, thyroidectomy in these patients is conducted principally with diagnostic intent. Clinical models predictive of malignancy risk are needed to support treatment decisions in patients with thyroid nodules in order to reduce morbidity associated with unnecessary diagnostic surgery. Methods Data were analyzed from a completed prospective cohort trial conducted over a 4-year period involving 216 patients with thyroid nodules undergoing ultrasound (US, electrical impedance scanning (EIS and fine needle aspiration cytology (FNA prior to thyroidectomy. A Bayesian model was designed to predict malignancy in thyroid nodules based on multivariate dependence relationships between independent covariates. Ten-fold cross-validation was performed to estimate classifier error wherein the data set was randomized into ten separate and unique train and test sets consisting of a training set (90% of records and a test set (10% of records. A receiver-operating-characteristics (ROC curve of these predictions and area under the curve (AUC were calculated to determine model robustness for predicting malignancy in thyroid nodules. Results Thyroid nodule size, FNA cytology, US and EIS characteristics were highly predictive of

  4. NASA Wrangler: Automated Cloud-Based Data Assembly in the RECOVER Wildfire Decision Support System

    Science.gov (United States)

    Schnase, John; Carroll, Mark; Gill, Roger; Wooten, Margaret; Weber, Keith; Blair, Kindra; May, Jeffrey; Toombs, William

    2017-01-01

    NASA Wrangler is a loosely-coupled, event driven, highly parallel data aggregation service designed to take advantageof the elastic resource capabilities of cloud computing. Wrangler automatically collects Earth observational data, climate model outputs, derived remote sensing data products, and historic biophysical data for pre-, active-, and post-wildfire decision making. It is a core service of the RECOVER decision support system, which is providing rapid-response GIS analytic capabilities to state and local government agencies. Wrangler reduces to minutes the time needed to assemble and deliver crucial wildfire-related data.

  5. Appreciative inquiry enhances cardiology nurses’ clinical decision making when using a clinical guideline on delirium

    DEFF Research Database (Denmark)

    Vedsegaard, Helle; Schrader, Anne-Marie; Rom, Gitte

    2016-01-01

    The current study responds to implementation challenges with translating evidence-based knowledge into practice. We explore how appreciative inquiry can be used in in-house learning sessions for nurses to enhance their knowledge in using a guideline on delirium as part of clinical decision making....... Through 18 sessions with 3–12 nurses, an appreciative inquiry approach was used. Specialist nurses from the Heart Centre of Copenhagen and senior lecturers from the Department of Nursing at Metropolitan University College facilitated the sessions. Field notes from the sessions were analysed using open...... and axial coding drawing on the principles of grounded theory. The study shows that appreciative inquiry was meaningful to cardiology nurses in providing them with knowledge of using a guideline on delirium in clinical decision making, the main reasons being a) data on a current patient were included, b...

  6. Evaluation of an automated knowledge-based textual summarization system for longitudinal clinical data, in the intensive care domain.

    Science.gov (United States)

    Goldstein, Ayelet; Shahar, Yuval; Orenbuch, Efrat; Cohen, Matan J

    2017-10-01

    To examine the feasibility of the automated creation of meaningful free-text summaries of longitudinal clinical records, using a new general methodology that we had recently developed; and to assess the potential benefits to the clinical decision-making process of using such a method to generate draft letters that can be further manually enhanced by clinicians. We had previously developed a system, CliniText (CTXT), for automated summarization in free text of longitudinal medical records, using a clinical knowledge base. In the current study, we created an Intensive Care Unit (ICU) clinical knowledge base, assisted by two ICU clinical experts in an academic tertiary hospital. The CTXT system generated free-text summary letters from the data of 31 different patients, which were compared to the respective original physician-composed discharge letters. The main evaluation measures were (1) relative completeness, quantifying the data items missed by one of the letters but included by the other, and their importance; (2) quality parameters, such as readability; (3) functional performance, assessed by the time needed, by three clinicians reading each of the summaries, to answer five key questions, based on the discharge letter (e.g., "What are the patient's current respiratory requirements?"), and by the correctness of the clinicians' answers. Completeness: In 13/31 (42%) of the letters the number of important items missed in the CTXT-generated letter was actually less than or equal to the number of important items missed by the MD-composed letter. In each of the MD-composed letters, at least two important items that were mentioned by the CTXT system were missed (a mean of 7.2±5.74). In addition, the standard deviation in the number of missed items in the MD letters (STD=15.4) was much higher than the standard deviation in the CTXT-generated letters (STD=5.3). Quality: The MD-composed letters obtained a significantly better grade in three out of four measured parameters

  7. Automated Proton Track Identification in MicroBooNE Using Gradient Boosted Decision Trees

    Energy Technology Data Exchange (ETDEWEB)

    Woodruff, Katherine [New Mexico State U.

    2017-10-02

    MicroBooNE is a liquid argon time projection chamber (LArTPC) neutrino experiment that is currently running in the Booster Neutrino Beam at Fermilab. LArTPC technology allows for high-resolution, three-dimensional representations of neutrino interactions. A wide variety of software tools for automated reconstruction and selection of particle tracks in LArTPCs are actively being developed. Short, isolated proton tracks, the signal for low- momentum-transfer neutral current (NC) elastic events, are easily hidden in a large cosmic background. Detecting these low-energy tracks will allow us to probe interesting regions of the proton's spin structure. An effective method for selecting NC elastic events is to combine a highly efficient track reconstruction algorithm to find all candidate tracks with highly accurate particle identification using a machine learning algorithm. We present our work on particle track classification using gradient tree boosting software (XGBoost) and the performance on simulated neutrino data.

  8. Data-driven approaches to decision making in automated tumor grading. An example of astrocytoma grading.

    Science.gov (United States)

    Kolles, H; von Wangenheim, A; Rahmel, J; Niedermayer, I; Feiden, W

    1996-08-01

    To compare four data-driven approaches to automated tumor grading based on morphometric data. Apart from the statistical procedure of linear discriminant analysis, three other approaches from the field of neural computing were evaluated. The numerical basis of this study was computed tomography-guided, stereotactically obtained astrocytoma biopsies from 86 patients colored with a combination of Feulgen and immunhistochemical Ki-67 (MIB1) staining. In these biopsies the cell nuclei in four consecutive fields of vision were evaluated morphometrically and the following parameters determined: relative nuclei area, secant lengths of the minimal spanning trees and relative volume-weighted mean nuclear volumes of the proliferating nuclei. Based on the analysis of these morphometric features, the multivariate-generated HOM grading system provides the highest correct grading rates (> 90%), whereas the two widely employed qualitative histologic grading systems for astrocytomas yield correct grading rates of about 60%. For automated tumor grading all approaches yield similar grading results; however, back-propagation networks provide reliable results only following an extensive training phase, which requires the use of a supercomputer. All other neurocomputing models can be run on simple UNIX workstations (AT&T, U.S.A). In contrast to discriminant analysis, backpropagation and Kohonen networks, the newly developed neural network architecture model of self-editing nearest neighbor nets (SEN3) provides incremental learning; i.e., the training phase does not need to be restarted each time when there is further information to learn. Trained SEN3 networks can be considered ready-to-use knowledge bases and are appropriate to integrating further morphometric data in a dynamic process that enhances the diagnostic power of such a network.

  9. Automated astatination of biomolecules - a stepping stone towards multicenter clinical trials

    DEFF Research Database (Denmark)

    Aneheim, Emma; Albertsson, Per; Bäck, Tom

    2015-01-01

    To facilitate multicentre clinical studies on targeted alpha therapy, it is necessary to develop an automated, on-site procedure for conjugating rare, short-lived, alpha-emitting radionuclides to biomolecules. Astatine-211 is one of the few alpha-emitting nuclides with appropriate chemical...... and physical properties for use in targeted therapies for cancer. Due to the very short range of the emitted α-particles, this therapy is particularly suited to treating occult, disseminated cancers. Astatine is not intrinsically tumour-specific; therefore, it requires an appropriate tumour-specific targeting...... vector, which can guide the radiation to the cancer cells. Consequently, an appropriate method is required for coupling the nuclide to the vector. To increase the availability of astatine-211 radiopharmaceuticals for targeted alpha therapy, their production should be automated. Here, we present a method...

  10. Encounter Decision Aid vs. Clinical Decision Support or Usual Care to Support Patient-Centered Treatment Decisions in Osteoporosis: The Osteoporosis Choice Randomized Trial II.

    Directory of Open Access Journals (Sweden)

    Annie LeBlanc

    Full Text Available Osteoporosis Choice, an encounter decision aid, can engage patients and clinicians in shared decision making about osteoporosis treatment. Its effectiveness compared to the routine provision to clinicians of the patient's estimated risk of fracture using the FRAX calculator is unknown.Patient-level, randomized, three-arm trial enrolling women over 50 with osteopenia or osteoporosis eligible for treatment with bisphosphonates, where the use of Osteoporosis Choice was compared to FRAX only and to usual care to determine impact on patient knowledge, decisional conflict, involvement in the decision-making process, decision to start and adherence to bisphosphonates.We enrolled 79 women in the three arms. Because FRAX estimation alone and usual care produced similar results, we grouped them for analysis. Compared to these, use of Osteoporosis Choice increased patient knowledge (median score 6 vs. 4, p = .01, improved understanding of fracture risk and risk reduction with bisphosphonates (p = .01 and p<.0001, respectively, had no effect on decision conflict, and increased patient engagement in the decision making process (OPTION scores 57% vs. 43%, p = .001. Encounters with the decision aid were 0.8 minutes longer (range: 33 minutes shorter to 3.0 minutes longer. There were twice as many patients receiving and filling prescriptions in the decision aid arm (83% vs. 40%, p = .07; medication adherence at 6 months was no different across arms.Supporting both patients and clinicians during the clinical encounter with the Osteoporosis Choice decision aid efficiently improves treatment decision making when compared to usual care with or without clinical decision support with FRAX results.clinical trials.gov NCT00949611.

  11. Clinical decision making in restorative dentistry, endodontics, and antibiotic prescription.

    Science.gov (United States)

    Zadik, Yehuda; Levin, Liran

    2008-01-01

    The purpose of this study was to evaluate the influence of geographic location of graduation (Israel, Eastern Europe, Latin America) on decision making regarding management of dental caries, periapical lesions, and antibiotic prescribing routines. A questionnaire was given to ninety-eight general practitioners regarding demographic and work habits. Photographs of lesions were shown on a screen. Participants reported recommended treatment and whether they would routinely prescribe antibiotics following regular endodontic treatment, retreatment, and impacted third molar surgical extraction in healthy patients. There was a 94 percent (n=92) response rate, of which eighty-five responses were used in the data analysis. Surgical treatment of asymptomatic enamel caries lesions was not recommended by most of the subjects, and surgery was recommended for DEJ caries lesions in low or moderate caries risk patients, both without significant differences between geographic regions of dental school graduation. Israelis had a lower frequency of retreatment in asymptomatic teeth that demonstrated periapical radiolucency with post restoration (without crown) compared to Latin Americans and East Europeans. Most of the participants would not retreat asymptomatic teeth that demonstrated periapical radiolucency with post and crown. After third molar surgery, 46 percent of participants routinely prescribed antibiotics. Significantly more Latin American graduates prescribed antibiotics following endodontic treatment, retreatment, and third molar extractions (pantibiotics) and overtreatment (caries) among young practitioners reflect failure of undergraduate education in proper use of antibiotics and management of the carious lesions according to the patient's clinical presentation and caries risk assessment rather than routinely undertaking surgical caries treatment.

  12. Clinical decision support systems at the Vienna General Hospital using Arden Syntax: Design, implementation, and integration.

    Science.gov (United States)

    Schuh, Christian; de Bruin, Jeroen S; Seeling, Walter

    2015-12-01

    The Allgemeines Krankenhaus Informations Management (AKIM) project was started at the Vienna General Hospital (VGH) several years ago. This led to the introduction of a new hospital information system (HIS), and the installation of the expert system platform (EXP) for the integration of Arden-Syntax-based clinical decision support systems (CDSSs). In this report we take a look at the milestones achieved and the challenges faced in the creation and modification of CDSSs, and their integration into the HIS over the last three years. We introduce a three-stage development method, which is followed in nearly all CDSS projects at the Medical University of Vienna and the VGH. Stage one comprises requirements engineering and system conception. Stage two focuses on the implementation and testing of the system. Finally, stage three describes the deployment and integration of the system in the VGH HIS. The HIS provides a clinical work environment for healthcare specialists using customizable graphical interfaces known as parametric medical documents. Multiple Arden Syntax servers are employed to host and execute the CDSS knowledge bases: two embedded in the EXP for production and development, and a further three in clinical routine for production, development, and quality assurance. Three systems are discussed; the systems serve different purposes in different clinical areas, but are all implemented with Arden Syntax. MONI-ICU is an automated surveillance system for monitoring healthcare-associated infections in the intensive care setting. TSM-CDS is a CDSS used for risk prediction in the formation of cutaneous melanoma metastases. Finally, TacroDS is a CDSS for the manipulation of dosages for tacrolimus, an immunosuppressive agent used after kidney transplantation. Problems in development and integration were related to data quality or availability, although organizational difficulties also caused delays in development and integration. Since the inception of the AKIM

  13. RECOVER: An Automated Cloud-Based Decision Support System for Post-fire Rehabilitation Planning

    Science.gov (United States)

    Schnase, John L.; Carroll, Mark; Weber, K. T.; Brown, Molly E.; Gill, Roger L.; Wooten, Margaret; May J.; Serr, K.; Smith, E.; Goldsby, R.; hide

    2014-01-01

    RECOVER is a site-specific decision support system that automatically brings together in a single analysis environment the information necessary for post-fire rehabilitation decision-making. After a major wildfire, law requires that the federal land management agencies certify a comprehensive plan for public safety, burned area stabilization, resource protection, and site recovery. These burned area emergency response (BAER) plans are a crucial part of our national response to wildfire disasters and depend heavily on data acquired from a variety of sources. Final plans are due within 21 days of control of a major wildfire and become the guiding document for managing the activities and budgets for all subsequent remediation efforts. There are few instances in the federal government where plans of such wide-ranging scope and importance are assembled on such short notice and translated into action more quickly. RECOVER has been designed in close collaboration with our agency partners and directly addresses their high-priority decision-making requirements. In response to a fire detection event, RECOVER uses the rapid resource allocation capabilities of cloud computing to automatically collect Earth observational data, derived decision products, and historic biophysical data so that when the fire is contained, BAER teams will have a complete and ready-to-use RECOVER dataset and GIS analysis environment customized for the target wildfire. Initial studies suggest that RECOVER can transform this information-intensive process by reducing from days to a matter of minutes the time required to assemble and deliver crucial wildfire-related data.

  14. Integration of Automated Decision Support Systems with Data Mining Abstract: A Client Perspective

    OpenAIRE

    Abdullah Saad AL-Malaise

    2013-01-01

    Customer’s behavior and satisfaction are always play important role to increase organization’s growth and market value. Customers are on top priority for the growing organization to build up their businesses. In this paper presents the architecture of Decision Support Systems (DSS) in connection to deal with the customer’s enquiries and requests. Main purpose behind the proposed model is to enhance the customer’s satisfaction and behavior using DSS. We proposed model by extension in tradition...

  15. Clinical Decision Support-based Quality Measurement (CDS-QM) Framework: Prototype Implementation, Evaluation, and Future Directions

    Science.gov (United States)

    Kukhareva, Polina V; Kawamoto, Kensaku; Shields, David E; Barfuss, Darryl T; Halley, Anne M; Tippetts, Tyler J; Warner, Phillip B; Bray, Bruce E; Staes, Catherine J

    2014-01-01

    Electronic quality measurement (QM) and clinical decision support (CDS) are closely related but are typically implemented independently, resulting in significant duplication of effort. While it seems intuitive that technical approaches could be re-used across these two related use cases, such reuse is seldom reported in the literature, especially for standards-based approaches. Therefore, we evaluated the feasibility of using a standards-based CDS framework aligned with anticipated EHR certification criteria to implement electronic QM. The CDS-QM framework was used to automate a complex national quality measure (SCIP-VTE-2) at an academic healthcare system which had previously relied on time-consuming manual chart abstractions. Compared with 305 manually-reviewed reference cases, the recall of automated measurement was 100%. The precision was 96.3% (CI:92.6%-98.5%) for ascertaining the denominator and 96.2% (CI:92.3%-98.4%) for the numerator. We therefore validated that a standards-based CDS-QM framework can successfully enable automated QM, and we identified benefits and challenges with this approach. PMID:25954389

  16. Creating and sharing clinical decision support content with Web 2.0: Issues and examples.

    Science.gov (United States)

    Wright, Adam; Bates, David W; Middleton, Blackford; Hongsermeier, Tonya; Kashyap, Vipul; Thomas, Sean M; Sittig, Dean F

    2009-04-01

    Clinical decision support is a powerful tool for improving healthcare quality and patient safety. However, developing a comprehensive package of decision support interventions is costly and difficult. If used well, Web 2.0 methods may make it easier and less costly to develop decision support. Web 2.0 is characterized by online communities, open sharing, interactivity and collaboration. Although most previous attempts at sharing clinical decision support content have worked outside of the Web 2.0 framework, several initiatives are beginning to use Web 2.0 to share and collaborate on decision support content. We present case studies of three efforts: the Clinfowiki, a world-accessible wiki for developing decision support content; Partners Healthcare eRooms, web-based tools for developing decision support within a single organization; and Epic Systems Corporation's Community Library, a repository for sharing decision support content for customers of a single clinical system vendor. We evaluate the potential of Web 2.0 technologies to enable collaborative development and sharing of clinical decision support systems through the lens of three case studies; analyzing technical, legal and organizational issues for developers, consumers and organizers of clinical decision support content in Web 2.0. We believe the case for Web 2.0 as a tool for collaborating on clinical decision support content appears strong, particularly for collaborative content development within an organization.

  17. Digital technology and clinical decision making in depression treatment: Current findings and future opportunities.

    Science.gov (United States)

    Hallgren, Kevin A; Bauer, Amy M; Atkins, David C

    2017-06-01

    Clinical decision making encompasses a broad set of processes that contribute to the effectiveness of depression treatments. There is emerging interest in using digital technologies to support effective and efficient clinical decision making. In this paper, we provide "snapshots" of research and current directions on ways that digital technologies can support clinical decision making in depression treatment. Practical facets of clinical decision making are reviewed, then research, design, and implementation opportunities where technology can potentially enhance clinical decision making are outlined. Discussions of these opportunities are organized around three established movements designed to enhance clinical decision making for depression treatment, including measurement-based care, integrated care, and personalized medicine. Research, design, and implementation efforts may support clinical decision making for depression by (1) improving tools to incorporate depression symptom data into existing electronic health record systems, (2) enhancing measurement of treatment fidelity and treatment processes, (3) harnessing smartphone and biosensor data to inform clinical decision making, (4) enhancing tools that support communication and care coordination between patients and providers and within provider teams, and (5) leveraging treatment and outcome data from electronic health record systems to support personalized depression treatment. The current climate of rapid changes in both healthcare and digital technologies facilitates an urgent need for research, design, and implementation of digital technologies that explicitly support clinical decision making. Ensuring that such tools are efficient, effective, and usable in frontline treatment settings will be essential for their success and will require engagement of stakeholders from multiple domains. © 2017 Wiley Periodicals, Inc.

  18. Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening

    Science.gov (United States)

    Brandt, Kathleen R.; Scott, Christopher G.; Ma, Lin; Mahmoudzadeh, Amir P.; Jensen, Matthew R.; Whaley, Dana H.; Wu, Fang Fang; Malkov, Serghei; Hruska, Carrie B.; Norman, Aaron D.; Heine, John; Shepherd, John; Pankratz, V. Shane; Kerlikowske, Karla

    2016-01-01

    Purpose To compare the classification of breast density with two automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.0; Hologic, Bedford, Mass), with clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications and to examine associations of these measures with breast cancer risk. Materials and Methods In this study, 1911 patients with breast cancer and 4170 control subjects matched for age, race, examination date, and mammography machine were evaluated. Participants underwent mammography at Mayo Clinic or one of four sites within the San Francisco Mammography Registry between 2006 and 2012 and provided informed consent or a waiver for research, in compliance with HIPAA regulations and institutional review board approval. Digital mammograms were retrieved a mean of 2.1 years (range, 6 months to 6 years) before cancer diagnosis, with the corresponding clinical BI-RADS density classifications, and Volpara and Quantra density estimates were generated. Agreement was assessed with weighted κ statistics among control subjects. Breast cancer associations were evaluated with conditional logistic regression, adjusted for age and body mass index. Odds ratios, C statistics, and 95% confidence intervals (CIs) were estimated. Results Agreement between clinical BI-RADS density classifications and Volpara and Quantra BI-RADS estimates was moderate, with κ values of 0.57 (95% CI: 0.55, 0.59) and 0.46 (95% CI: 0.44, 0.47), respectively. Differences of up to 14% in dense tissue classification were found, with Volpara classifying 51% of women as having dense breasts, Quantra classifying 37%, and clinical BI-RADS assessment used to classify 43%. Clinical and automated measures showed similar breast cancer associations; odds ratios for extremely dense breasts versus scattered fibroglandular densities were 1.8 (95% CI: 1.5, 2.2), 1.9 (95% CI: 1.5, 2.5), and 2.3 (95% CI: 1.9, 2.8) for Volpara, Quantra

  19. An exploration of the correlates of nurse practitioners' clinical decision-making abilities.

    Science.gov (United States)

    Chen, Shiah-Lian; Hsu, Hsiu-Ying; Chang, Chin-Fu; Lin, Esther Ching-Lan

    2016-04-01

    This study investigated nurse practitioners' clinical decision-making abilities and the factors that affect these abilities. Nurse practitioners play an important role in clinical care decision-making; however, studies exploring the factors that affect their decision-making abilities are lacking. A cross-sectional descriptive survey was employed. A purposive sample of 197 nurse practitioners was recruited from a medical centre in central Taiwan. Structured questionnaires consisting of the Knowledge Readiness Scale, the Critical Thinking Disposition Inventory and the Clinical Decision-Making Model Inventory were used to collect data. The intuitive-analytical type was the most commonly used decision-making model, and the intuitive type was the least frequently used model. The decision-making model used was significantly related to the nurse practitioners' work unit. Significant differences were noted between the nurse practitioners' clinical decision-making models and their critical thinking dispositions (openness and empathy). The nurse practitioners' years of work experience, work unit, professional knowledge and critical thinking disposition (openness and empathy as well as holistic and reflective dispositions) predicted the nurse practitioners' analytical decision-making scores. Age, years of nurse practitioner work experience, work unit and critical thinking disposition (holistic and reflective) predicted the nurse practitioners' intuitive decision-making scores. This study contributes to the topic of clinical decision-making by describing various types of nurse practitioner decision-making. The factors associated with analytic and intuitive decision-making scores were identified. These findings might be beneficial when planning continuing education programmes to enhance the clinical decision-making abilities of nurse practitioners. The study results showed that nurse practitioners demonstrated various clinical decision-making types across different work units

  20. Parametric vs. Nonparametric Regression Modelling within Clinical Decision Support

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan; Zvárová, Jana

    2017-01-01

    Roč. 5, č. 1 (2017), s. 21-27 ISSN 1805-8698 R&D Projects: GA ČR GA17-01251S Institutional support: RVO:67985807 Keywords : decision support systems * decision rules * statistical analysis * nonparametric regression Subject RIV: IN - Informatics, Computer Science OBOR OECD: Statistics and probability

  1. Multistage decision-based heart sound delineation method for automated analysis of heart sounds and murmurs.

    Science.gov (United States)

    Nivitha Varghees, V; Ramachandran, K I

    2015-12-01

    A robust multistage decision-based heart sound delineation (MDHSD) method is presented for automatically determining the boundaries and peaks of heart sounds (S1, S2, S3, and S4), systolic, and diastolic murmurs (early, mid, and late) and high-pitched sounds (HPSs) of the phonocardiogram (PCG) signal. The proposed MDHSD method consists of the Gaussian kernels based signal decomposition (GSDs) and multistage decision-based delineation (MDBD). The GSD algorithm first removes the low-frequency (LF) artefacts and then decomposes the filtered signal into two subsignals: the LF sound part (S1, S2, S3, and S4) and the high-frequency sound part (murmurs and HPSs). The MDBD algorithm consists of absolute envelope extraction, adaptive thresholding, and fiducial point determination. The accuracy and robustness of the proposed method is evaluated using various types of normal and pathological PCG signals. Results show that the method achieves an average sensitivity of 98.22%, positive predictivity of 97.46%, and overall accuracy of 95.78%. The method yields maximum average delineation errors of 4.52 and 4.14 ms for determining the start-point and end-point of sounds. The proposed multistage delineation algorithm is capable of improving the delineation accuracy under time-varying amplitudes of heart sounds and various types of murmurs. The proposed method has significant potential applications in heart sounds and murmurs classification systems.

  2. Conventional Versus Automated Implantation of Loose Seeds in Prostate Brachytherapy: Analysis of Dosimetric and Clinical Results

    Energy Technology Data Exchange (ETDEWEB)

    Genebes, Caroline, E-mail: genebes.caroline@claudiusregaud.fr [Radiation Oncology Department, Institut Claudius Regaud, Toulouse (France); Filleron, Thomas; Graff, Pierre [Radiation Oncology Department, Institut Claudius Regaud, Toulouse (France); Jonca, Frédéric [Department of Urology, Clinique Ambroise Paré, Toulouse (France); Huyghe, Eric; Thoulouzan, Matthieu; Soulie, Michel; Malavaud, Bernard [Department of Urology and Andrology, CHU Rangueil, Toulouse (France); Aziza, Richard; Brun, Thomas; Delannes, Martine; Bachaud, Jean-Marc [Radiation Oncology Department, Institut Claudius Regaud, Toulouse (France)

    2013-11-15

    Purpose: To review the clinical outcome of I-125 permanent prostate brachytherapy (PPB) for low-risk and intermediate-risk prostate cancer and to compare 2 techniques of loose-seed implantation. Methods and Materials: 574 consecutive patients underwent I-125 PPB for low-risk and intermediate-risk prostate cancer between 2000 and 2008. Two successive techniques were used: conventional implantation from 2000 to 2004 and automated implantation (Nucletron, FIRST system) from 2004 to 2008. Dosimetric and biochemical recurrence-free (bNED) survival results were reported and compared for the 2 techniques. Univariate and multivariate analysis researched independent predictors for bNED survival. Results: 419 (73%) and 155 (27%) patients with low-risk and intermediate-risk disease, respectively, were treated (median follow-up time, 69.3 months). The 60-month bNED survival rates were 95.2% and 85.7%, respectively, for patients with low-risk and intermediate-risk disease (P=.04). In univariate analysis, patients treated with automated implantation had worse bNED survival rates than did those treated with conventional implantation (P<.0001). By day 30, patients treated with automated implantation showed lower values of dose delivered to 90% of prostate volume (D90) and volume of prostate receiving 100% of prescribed dose (V100). In multivariate analysis, implantation technique, Gleason score, and V100 on day 30 were independent predictors of recurrence-free status. Grade 3 urethritis and urinary incontinence were observed in 2.6% and 1.6% of the cohort, respectively, with no significant differences between the 2 techniques. No grade 3 proctitis was observed. Conclusion: Satisfactory 60-month bNED survival rates (93.1%) and acceptable toxicity (grade 3 urethritis <3%) were achieved by loose-seed implantation. Automated implantation was associated with worse dosimetric and bNED survival outcomes.

  3. Conventional Versus Automated Implantation of Loose Seeds in Prostate Brachytherapy: Analysis of Dosimetric and Clinical Results

    International Nuclear Information System (INIS)

    Genebes, Caroline; Filleron, Thomas; Graff, Pierre; Jonca, Frédéric; Huyghe, Eric; Thoulouzan, Matthieu; Soulie, Michel; Malavaud, Bernard; Aziza, Richard; Brun, Thomas; Delannes, Martine; Bachaud, Jean-Marc

    2013-01-01

    Purpose: To review the clinical outcome of I-125 permanent prostate brachytherapy (PPB) for low-risk and intermediate-risk prostate cancer and to compare 2 techniques of loose-seed implantation. Methods and Materials: 574 consecutive patients underwent I-125 PPB for low-risk and intermediate-risk prostate cancer between 2000 and 2008. Two successive techniques were used: conventional implantation from 2000 to 2004 and automated implantation (Nucletron, FIRST system) from 2004 to 2008. Dosimetric and biochemical recurrence-free (bNED) survival results were reported and compared for the 2 techniques. Univariate and multivariate analysis researched independent predictors for bNED survival. Results: 419 (73%) and 155 (27%) patients with low-risk and intermediate-risk disease, respectively, were treated (median follow-up time, 69.3 months). The 60-month bNED survival rates were 95.2% and 85.7%, respectively, for patients with low-risk and intermediate-risk disease (P=.04). In univariate analysis, patients treated with automated implantation had worse bNED survival rates than did those treated with conventional implantation (P<.0001). By day 30, patients treated with automated implantation showed lower values of dose delivered to 90% of prostate volume (D90) and volume of prostate receiving 100% of prescribed dose (V100). In multivariate analysis, implantation technique, Gleason score, and V100 on day 30 were independent predictors of recurrence-free status. Grade 3 urethritis and urinary incontinence were observed in 2.6% and 1.6% of the cohort, respectively, with no significant differences between the 2 techniques. No grade 3 proctitis was observed. Conclusion: Satisfactory 60-month bNED survival rates (93.1%) and acceptable toxicity (grade 3 urethritis <3%) were achieved by loose-seed implantation. Automated implantation was associated with worse dosimetric and bNED survival outcomes

  4. Clinical Evaluation of the First Automated Assay for the Detection of Stimulating TSH Receptor Autoantibodies.

    Science.gov (United States)

    Allelein, S; Ehlers, M; Goretzki, S; Hermsen, D; Feldkamp, J; Haase, M; Dringenberg, T; Schmid, C; Hautzel, H; Schott, M

    2016-12-01

    Until recently, stimulating TSH receptor autoantibodies (sTRAbs) could only be measured by bioassays. A new assay system, which directly detects sTRAb in sera by applying bridge technology, has been established and is now available as automated chemiluminescence (bridge) immunoassay. We evaluated the automated bridge assay in clinical routine and compared it with a conventional automated TRAb assay (competition assay). Altogether, 226 Graves' disease (GD), 57 autoimmune thyroiditis, 74 non-autoimmune nodular goiter and 49 thyroid cancer patients, as well as 41 healthy controls were retrospectively evaluated. ROC plot analysis based on sera of newly diagnosed GD patients revealed an area under curve of 0.99 (95% CI: 0.99-1.0) indicating a high assay sensitivity and specificity. The highest sensitivity (100%) and specificity (99%) were seen at a cut-off level of 0.55 IU/l. The calculated positive predictive value was 94%, whereas the negative was 100%. Applying a ROC plot-derived cut-off of≥0.30 IU/l, derived from sera of GD patients already receiving antithyroid drug therapy for≤6 months, the sensitivity was 99% whereas the specificity was 98%. Detailed comparison of both assay systems used revealed a slightly different distribution of sTRAb and TRAb. Measurement of sTRAb during follow-up revealed a steady decline over one year of follow-up. In summary, our results demonstrate that the new automated bridge assay system for detecting sTRAb has a high sensitivity and specificity for diagnosing GD and to discriminate from other thyroid diseases, respectively. Our study, however, does not provide full evidence that the bridge assay is specific for sTRAb only. © Georg Thieme Verlag KG Stuttgart · New York.

  5. Automated radiosynthesis of [{sup 11}C]morphine for clinical investigation

    Energy Technology Data Exchange (ETDEWEB)

    Fan Jinda [Department of Radiology, Washington University School of Medicine, 510 South Kingshighway Blvd. St. Louis, MO 63110 (United States); Meissner, Konrad [Department of Anesthesiology, Washington University School of Medicine, 510 South Kingshighway Blvd. St. Louis, MO 63110 (United States); Gaehle, Gregory G.; Li Shihong [Department of Radiology, Washington University School of Medicine, 510 South Kingshighway Blvd. St. Louis, MO 63110 (United States); Kharasch, Evan D. [Department of Anesthesiology, Washington University School of Medicine, 510 South Kingshighway Blvd. St. Louis, MO 63110 (United States); Mach, Robert H. [Department of Radiology, Washington University School of Medicine, 510 South Kingshighway Blvd. St. Louis, MO 63110 (United States); Tu Zhude, E-mail: tuz@mir.wustl.ed [Department of Radiology, Washington University School of Medicine, 510 South Kingshighway Blvd. St. Louis, MO 63110 (United States)

    2011-02-15

    To meet a multiple-dose clinical evaluation of the P-gp modulation of [{sup 11}C]morphine delivery into the human brain, radiosynthesis of [{sup 11}C]morphine was accomplished on an automated system by N-methylation of normorphine with [{sup 11}C]CH{sub 3}I. A methodology employing optimized solid phase extraction of the HPLC eluent was developed. Radiosynthesis took 45 min with a radiochemical yield ranging from 45% to 50% and specific activity ranging from 20 to 26 Ci/{mu}mol (decay corrected to end-of-bombardment); radiochemical and chemical purities were >95% (n=28).

  6. Vision 20/20: Automation and advanced computing in clinical radiation oncology

    International Nuclear Information System (INIS)

    Moore, Kevin L.; Moiseenko, Vitali; Kagadis, George C.; McNutt, Todd R.; Mutic, Sasa

    2014-01-01

    This Vision 20/20 paper considers what computational advances are likely to be implemented in clinical radiation oncology in the coming years and how the adoption of these changes might alter the practice of radiotherapy. Four main areas of likely advancement are explored: cloud computing, aggregate data analyses, parallel computation, and automation. As these developments promise both new opportunities and new risks to clinicians and patients alike, the potential benefits are weighed against the hazards associated with each advance, with special considerations regarding patient safety under new computational platforms and methodologies. While the concerns of patient safety are legitimate, the authors contend that progress toward next-generation clinical informatics systems will bring about extremely valuable developments in quality improvement initiatives, clinical efficiency, outcomes analyses, data sharing, and adaptive radiotherapy

  7. Automated EEG detection algorithms and clinical semiology in epilepsy: importance of correlations.

    Science.gov (United States)

    Hogan, R Edward

    2011-12-01

    With advances in technological innovation, electroencephalography has remained the gold standard for classification and localization of epileptic seizures. Like other diagnostic modalities, technological advances have opened new avenues for assessment of data, and hold great promise to improve interpretive capabilities. However, proper overall interpretation and application of electroencephalographic findings relies on valid correlations of associated clinical semiology. This article addresses interpretation of clinical signs and symptoms in the context of the diagnostic predictive value of electroencephalographic, clinical, and electrographic definitions of seizures, and upcoming challenges of interpreting intracranial high-frequency electroencephalographic data. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

  8. Vision 20/20: Automation and advanced computing in clinical radiation oncology.

    Science.gov (United States)

    Moore, Kevin L; Kagadis, George C; McNutt, Todd R; Moiseenko, Vitali; Mutic, Sasa

    2014-01-01

    This Vision 20/20 paper considers what computational advances are likely to be implemented in clinical radiation oncology in the coming years and how the adoption of these changes might alter the practice of radiotherapy. Four main areas of likely advancement are explored: cloud computing, aggregate data analyses, parallel computation, and automation. As these developments promise both new opportunities and new risks to clinicians and patients alike, the potential benefits are weighed against the hazards associated with each advance, with special considerations regarding patient safety under new computational platforms and methodologies. While the concerns of patient safety are legitimate, the authors contend that progress toward next-generation clinical informatics systems will bring about extremely valuable developments in quality improvement initiatives, clinical efficiency, outcomes analyses, data sharing, and adaptive radiotherapy.

  9. Quantifying explainable discrimination and removing illegal discrimination in automated decision making

    KAUST Repository

    Kamiran, Faisal

    2012-11-18

    Recently, the following discrimination-aware classification problem was introduced. Historical data used for supervised learning may contain discrimination, for instance, with respect to gender. The question addressed by discrimination-aware techniques is, given sensitive attribute, how to train discrimination-free classifiers on such historical data that are discriminative, with respect to the given sensitive attribute. Existing techniques that deal with this problem aim at removing all discrimination and do not take into account that part of the discrimination may be explainable by other attributes. For example, in a job application, the education level of a job candidate could be such an explainable attribute. If the data contain many highly educated male candidates and only few highly educated women, a difference in acceptance rates between woman and man does not necessarily reflect gender discrimination, as it could be explained by the different levels of education. Even though selecting on education level would result in more males being accepted, a difference with respect to such a criterion would not be considered to be undesirable, nor illegal. Current state-of-the-art techniques, however, do not take such gender-neutral explanations into account and tend to overreact and actually start reverse discriminating, as we will show in this paper. Therefore, we introduce and analyze the refined notion of conditional non-discrimination in classifier design. We show that some of the differences in decisions across the sensitive groups can be explainable and are hence tolerable. Therefore, we develop methodology for quantifying the explainable discrimination and algorithmic techniques for removing the illegal discrimination when one or more attributes are considered as explanatory. Experimental evaluation on synthetic and real-world classification datasets demonstrates that the new techniques are superior to the old ones in this new context, as they succeed in

  10. Research on Clinical Decisions Made Daily in Family Medicine.

    Science.gov (United States)

    Bowman, Marjorie A; Neale, Anne Victoria; Seehusen, Dean A

    2017-01-01

    This issue presents research on the types of decisions that are required daily in family medicine. Patients often make these health decisions, and family physicians help patients with these decisions daily. Patients and their family physicians discuss when to quit screening for colon cancer, which treatment to choose for localized prostate cancer, when to test for pertussis when a cough is present, whether to take prescribed medications, how to complete more preventive services, and how to understand the "new genetics", and family physician use of telehealth. © Copyright 2017 by the American Board of Family Medicine.

  11. External validation of clinical decision rules for children with wrist trauma

    NARCIS (Netherlands)

    Mulders, Marjolein A. M.; Walenkamp, Monique M. J.; Dubois, Bente F. H.; Slaar, Annelie; Goslings, J. Carel; Schep, Niels W. L.

    2017-01-01

    Clinical decision rules help to avoid potentially unnecessary radiographs of the wrist, reduce waiting times and save costs. The primary aim of this study was to provide an overview of all existing non-validated clinical decision rules for wrist trauma in children and to externally validate these

  12. External validation of clinical decision rules for children with wrist trauma

    NARCIS (Netherlands)

    M.A.M. Mulders (Marjolein A. M.); M.M.J. Walenkamp (Monique); B.F.H. Dubois (Bente F. H.); A. Slaar (Annelie); J.C. Goslings (Carel); N.W.L. Schep (Niels)

    2017-01-01

    textabstractBackground: Clinical decision rules help to avoid potentially unnecessary radiographs of the wrist, reduce waiting times and save costs. Objective: The primary aim of this study was to provide an overview of all existing non-validated clinical decision rules for wrist trauma in children

  13. PredicT-ML: a tool for automating machine learning model building with big clinical data.

    Science.gov (United States)

    Luo, Gang

    2016-01-01

    Predictive modeling is fundamental to transforming large clinical data sets, or "big clinical data," into actionable knowledge for various healthcare applications. Machine learning is a major predictive modeling approach, but two barriers make its use in healthcare challenging. First, a machine learning tool user must choose an algorithm and assign one or more model parameters called hyper-parameters before model training. The algorithm and hyper-parameter values used typically impact model accuracy by over 40 %, but their selection requires many labor-intensive manual iterations that can be difficult even for computer scientists. Second, many clinical attributes are repeatedly recorded over time, requiring temporal aggregation before predictive modeling can be performed. Many labor-intensive manual iterations are required to identify a good pair of aggregation period and operator for each clinical attribute. Both barriers result in time and human resource bottlenecks, and preclude healthcare administrators and researchers from asking a series of what-if questions when probing opportunities to use predictive models to improve outcomes and reduce costs. This paper describes our design of and vision for PredicT-ML (prediction tool using machine learning), a software system that aims to overcome these barriers and automate machine learning model building with big clinical data. The paper presents the detailed design of PredicT-ML. PredicT-ML will open the use of big clinical data to thousands of healthcare administrators and researchers and increase the ability to advance clinical research and improve healthcare.

  14. Enhancing clinical decision making: development of a contiguous definition and conceptual framework.

    Science.gov (United States)

    Tiffen, Jennifer; Corbridge, Susan J; Slimmer, Lynda

    2014-01-01

    Clinical decision making is a term frequently used to describe the fundamental role of the nurse practitioner; however, other terms have been used interchangeably. The purpose of this article is to begin the process of developing a definition and framework of clinical decision making. The developed definition was "Clinical decision making is a contextual, continuous, and evolving process, where data are gathered, interpreted, and evaluated in order to select an evidence-based choice of action." A contiguous framework for clinical decision making specific for nurse practitioners is also proposed. Having a clear and unique understanding of clinical decision making will allow for consistent use of the term, which is relevant given the changing educational requirements for nurse practitioners and broadening scope of practice. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Automated astatination of biomolecules - a stepping stone towards multicenter clinical trials

    Science.gov (United States)

    Aneheim, Emma; Albertsson, Per; Bäck, Tom; Jensen, Holger; Palm, Stig; Lindegren, Sture

    2015-07-01

    To facilitate multicentre clinical studies on targeted alpha therapy, it is necessary to develop an automated, on-site procedure for conjugating rare, short-lived, alpha-emitting radionuclides to biomolecules. Astatine-211 is one of the few alpha-emitting nuclides with appropriate chemical and physical properties for use in targeted therapies for cancer. Due to the very short range of the emitted α-particles, this therapy is particularly suited to treating occult, disseminated cancers. Astatine is not intrinsically tumour-specific; therefore, it requires an appropriate tumour-specific targeting vector, which can guide the radiation to the cancer cells. Consequently, an appropriate method is required for coupling the nuclide to the vector. To increase the availability of astatine-211 radiopharmaceuticals for targeted alpha therapy, their production should be automated. Here, we present a method that combines dry distillation of astatine-211 and a synthesis module for producing radiopharmaceuticals into a process platform. This platform will standardize production of astatinated radiopharmaceuticals, and hence, it will facilitate large clinical studies focused on this promising, but chemically challenging, alpha-emitting radionuclide. In this work, we describe the process platform, and we demonstrate the production of both astaine-211, for preclinical use, and astatine-211 labelled antibodies.

  16. Automated Classification of Severity in Cardiac Dyssynchrony Merging Clinical Data and Mechanical Descriptors

    Directory of Open Access Journals (Sweden)

    Alejandro Santos-Díaz

    2017-01-01

    Full Text Available Cardiac resynchronization therapy (CRT improves functional classification among patients with left ventricle malfunction and ventricular electric conduction disorders. However, a high percentage of subjects under CRT (20%–30% do not show any improvement. Nonetheless the presence of mechanical contraction dyssynchrony in ventricles has been proposed as an indicator of CRT response. This work proposes an automated classification model of severity in ventricular contraction dyssynchrony. The model includes clinical data such as left ventricular ejection fraction (LVEF, QRS and P-R intervals, and the 3 most significant factors extracted from the factor analysis of dynamic structures applied to a set of equilibrium radionuclide angiography images representing the mechanical behavior of cardiac contraction. A control group of 33 normal volunteers (28±5 years, LVEF of 59.7%±5.8% and a HF group of 42 subjects (53.12±15.05 years, LVEF < 35% were studied. The proposed classifiers had hit rates of 90%, 50%, and 80% to distinguish between absent, mild, and moderate-severe interventricular dyssynchrony, respectively. For intraventricular dyssynchrony, hit rates of 100%, 50%, and 90% were observed distinguishing between absent, mild, and moderate-severe, respectively. These results seem promising in using this automated method for clinical follow-up of patients undergoing CRT.

  17. Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods.

    Science.gov (United States)

    Luo, Gang; Stone, Bryan L; Johnson, Michael D; Tarczy-Hornoch, Peter; Wilcox, Adam B; Mooney, Sean D; Sheng, Xiaoming; Haug, Peter J; Nkoy, Flory L

    2017-08-29

    To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient's weight kept rising in the past year). This process becomes infeasible with limited budgets. This study's goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data. This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care

  18. Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

    Science.gov (United States)

    Stone, Bryan L; Johnson, Michael D; Tarczy-Hornoch, Peter; Wilcox, Adam B; Mooney, Sean D; Sheng, Xiaoming; Haug, Peter J; Nkoy, Flory L

    2017-01-01

    Background To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient’s weight kept rising in the past year). This process becomes infeasible with limited budgets. Objective This study’s goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data. Methods This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new

  19. [International outcomes from attempts to implement a clinical decision support system in gastroenterology].

    Science.gov (United States)

    Tenório, Josceli Maria; Hummel, Anderson Diniz; Sdepanian, Vera Lucia; Pisa, Ivan Torres; de Fátima Marin, Heimar

    2011-01-01

    This study aimed at describing the recent experience acquired with the implementation and use of clinical decision support system in gastroenterology in order to determine the level of development, tests used and advantages that such a system can offer to the medical practice. A search in the PubMed, LILACS and ISI Web of Knowledge databases for studies in decision-making support systems in gastroenterology including original papers produced from 2005 to 2010 was performed. A total of 104 scientific papers were retrieved initially. These were analyzed using inclusion and exclusion criteria, thus yielding nine studies for further analysis. The clinical decision support system analyzed in the present study showed a great variety of clinical problems regarding the investigation of a disease and the determination of a diagnosis. Eighty-nine per cent of the studies showed experimental models for clinical decision support system development. Seventy-eight per cent of the studies described the outcomes obtained with artificial intelligence technique. Two studies compared the clinical decision support system performance with that of a doctor, and only one research work described a controlled study evidencing improvements in the medical practice. The studies analyzed showed evidence of potential benefits that clinical decision support system can bring to the clinical practice. However, further controlled studies performed in medical day-to-day conditions and environment should be performed in order to provide more clear evidence of the usefulness of clinical decision support system in the medical practice.

  20. Automated Manufacturing of Potent CD20-Directed Chimeric Antigen Receptor T Cells for Clinical Use.

    Science.gov (United States)

    Lock, Dominik; Mockel-Tenbrinck, Nadine; Drechsel, Katharina; Barth, Carola; Mauer, Daniela; Schaser, Thomas; Kolbe, Carolin; Al Rawashdeh, Wael; Brauner, Janina; Hardt, Olaf; Pflug, Natali; Holtick, Udo; Borchmann, Peter; Assenmacher, Mario; Kaiser, Andrew

    2017-10-01

    The clinical success of gene-engineered T cells expressing a chimeric antigen receptor (CAR), as manifested in several clinical trials for the treatment of B cell malignancies, warrants the development of a simple and robust manufacturing procedure capable of reducing to a minimum the challenges associated with its complexity. Conventional protocols comprise many open handling steps, are labor intensive, and are difficult to upscale for large numbers of patients. Furthermore, extensive training of personnel is required to avoid operator variations. An automated current Good Manufacturing Practice-compliant process has therefore been developed for the generation of gene-engineered T cells. Upon installation of the closed, single-use tubing set on the CliniMACS Prodigy™, sterile welding of the starting cell product, and sterile connection of the required reagents, T cells are magnetically enriched, stimulated, transduced using lentiviral vectors, expanded, and formulated. Starting from healthy donor (HD) or lymphoma or melanoma patient material (PM), the robustness and reproducibility of the manufacturing of anti-CD20 specific CAR T cells were verified. Independent of the starting material, operator, or device, the process consistently yielded a therapeutic dose of highly viable CAR T cells. Interestingly, the formulated product obtained with PM was comparable to that of HD with respect to cell composition, phenotype, and function, even though the starting material differed significantly. Potent antitumor reactivity of the produced anti-CD20 CAR T cells was shown in vitro as well as in vivo. In summary, the automated T cell transduction process meets the requirements for clinical manufacturing that the authors intend to use in two separate clinical trials for the treatment of melanoma and B cell lymphoma.

  1. Modeling information flows in clinical decision support: key insights for enhancing system effectiveness.

    Science.gov (United States)

    Medlock, Stephanie; Wyatt, Jeremy C; Patel, Vimla L; Shortliffe, Edward H; Abu-Hanna, Ameen

    2016-09-01

    A fundamental challenge in the field of clinical decision support is to determine what characteristics of systems make them effective in supporting particular types of clinical decisions. However, we lack such a theory of decision support itself and a model to describe clinical decisions and the systems to support them. This article outlines such a framework. We present a two-stream model of information flow within clinical decision-support systems (CDSSs): reasoning about the patient (the clinical stream), and reasoning about the user (the cognitive-behavioral stream). We propose that CDSS "effectiveness" be measured not only in terms of a system's impact on clinical care, but also in terms of how (and by whom) the system is used, its effect on work processes, and whether it facilitates appropriate decisions by clinicians and patients. Future research into which factors improve the effectiveness of decision support should not regard CDSSs as a single entity, but should instead differentiate systems based on their attributes, users, and the decision being supported. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  2. Automated Enrichment, Transduction, and Expansion of Clinical-Scale CD62L+ T Cells for Manufacturing of Gene Therapy Medicinal Products

    Science.gov (United States)

    Priesner, Christoph; Aleksandrova, Krasimira; Esser, Ruth; Mockel-Tenbrinck, Nadine; Leise, Jana; Drechsel, Katharina; Marburger, Michael; Quaiser, Andrea; Goudeva, Lilia; Arseniev, Lubomir; Kaiser, Andrew D.; Glienke, Wolfgang; Koehl, Ulrike

    2016-01-01

    Multiple clinical studies have demonstrated that adaptive immunotherapy using redirected T cells against advanced cancer has led to promising results with improved patient survival. The continuously increasing interest in those advanced gene therapy medicinal products (GTMPs) leads to a manufacturing challenge regarding automation, process robustness, and cell storage. Therefore, this study addresses the proof of principle in clinical-scale selection, stimulation, transduction, and expansion of T cells using the automated closed CliniMACS® Prodigy system. Naïve and central memory T cells from apheresis products were first immunomagnetically enriched using anti-CD62L magnetic beads and further processed freshly (n = 3) or split for cryopreservation and processed after thawing (n = 1). Starting with 0.5 × 108 purified CD3+ T cells, three mock runs and one run including transduction with green fluorescent protein (GFP)-containing vector resulted in a median final cell product of 16 × 108 T cells (32-fold expansion) up to harvesting after 2 weeks. Expression of CD62L was downregulated on T cells after thawing, which led to the decision to purify CD62L+CD3+ T cells freshly with cryopreservation thereafter. Most important in the split product, a very similar expansion curve was reached comparing the overall freshly CD62L selected cells with those after thawing, which could be demonstrated in the T cell subpopulations as well by showing a nearly identical conversion of the CD4/CD8 ratio. In the GFP run, the transduction efficacy was 83%. In-process control also demonstrated sufficient glucose levels during automated feeding and medium removal. The robustness of the process and the constant quality of the final product in a closed and automated system give rise to improve harmonized manufacturing protocols for engineered T cells in future gene therapy studies. PMID:27562135

  3. Influence of the sFlt-1/PlGF ratio on clinical decision-making in women with suspected preeclampsia--the PreOS study protocol.

    Science.gov (United States)

    Hund, Martin; Verhagen-Kamerbeek, Wilma; Reim, Manfred; Messinger, Diethelm; van der Does, Reinhard; Stepan, Holger

    2015-02-01

    To assess how routine clinical use of the Roche fully automated Elecsys® sFlt-1/PlGF test changes decision-making of physicians to hospitalize pregnant women with suspected preeclampsia. The Preeclampsia Open Study (PreOS) study is a multicenter, prospective, open-label, non-interventional study in 150 women showing signs and symptoms of preeclampsia (suspected preeclampsia). Physicians record their intended procedures before and after knowledge of participants' sFlt-1/PlGF ratio. The study is conducted at five investigational sites in Germany and Austria. The PreOS study will provide evidence on how sFlt-1/PlGF ratio testing influences clinical decision-making in women with suspected preeclampsia in real-world clinical practice.

  4. Decision-theoretic planning of clinical patient management

    NARCIS (Netherlands)

    Peek, Niels Bastiaan

    2000-01-01

    When a doctor is treating a patient, he is constantly facing decisions. From the externally visible signs and symptoms he must establish a hypothesis of what might be wrong with the patient; then he must decide whether additional diagnostic procedures are required to verify this hypothesis,

  5. Clinical decision support must be useful, functional is not enough

    DEFF Research Database (Denmark)

    Kortteisto, Tiina; Komulainen, Jorma; Mäkelä, Marjukka

    2012-01-01

    's intention to use eCDS. The decisive reason for using or not using the eCDS is its perceived usefulness. Functional characteristics such as speed and ease of use are important but alone these are not enough. Specific information technology, professional, patient and environment features can help or hinder...

  6. The Effect of Client Case Complexity on Clinical Decision Making

    NARCIS (Netherlands)

    Groenier, M.; Pieters, J.M.; Witteman, C.L.M.; Lehmann, S.R.S.

    2014-01-01

    In mental health care, clinicians' treatment decisions are expected to be based on the formulation (i.e., exploration of the causing and maintaining mechanisms) of the client's problems. Previous research showed two things: clinicians' case formulations mainly contain descriptive information instead

  7. The effect of client case complexity on clinical decision making

    NARCIS (Netherlands)

    Groenier, Marleen; Pieters, Julius Marie; Witteman, C.L.M.; Lehmann, Sonja

    2014-01-01

    In mental health care, clinicians’ treatment decisions are expected to be based on the formulation (i.e., exploration of the causing and maintaining mechanisms) of the client’s problems. Previous research showed two things: clinicians’ case formulations mainly contain descriptive information instead

  8. Decentralized automated dispensing devices: systematic review of clinical and economic impacts in hospitals.

    Science.gov (United States)

    Tsao, Nicole W; Lo, Clifford; Babich, Michele; Shah, Kieran; Bansback, Nick J

    2014-03-01

    Technologies have been developed over the past 20 years to automate the stages of drug distribution in hospitals, including ordering, dispensing, delivery, and administration of medications, in attempts to decrease medication error rates. Decentralized automated dispensing devices (ADDs) represent one such technology that is being adopted by hospitals across Canada, but the touted benefits, in terms of improved patient safety and cost savings, are increasingly being questioned. To summarize and evaluate the existing literature reporting the clinical and economic impacts of using decentralized ADDs in hospitals. A literature search was conducted in MEDLINE, Embase, and all evidence-based medicine databases for the years 1992 to 2012 to identify English-language articles reporting on the use of ADDs in hospital wards. All randomized controlled trials, observational studies, before-and-after studies, time series analyses, cost-effectiveness and cost-benefit analyses, and review articles were considered for inclusion. Studies evaluating pharmacy-based ADDs, such as bar code-based medication dispensing carousels, automated dispensing shelves, and combinations of various dispensing modalities, were excluded. Of 175 studies initially identified, 8 were retained for evidence synthesis. It appears that ADDs were effective in reducing medication storage errors and the time that nurses spent taking inventory of narcotics and controlled substances. There was no definitive evidence that using ADDs increased the time that nurses or pharmacists spent with patients, reduced medication errors resulting in patient harm, or reduced costs in Canadian hospitals. However, pharmacy technicians spent more time stocking the machines. ADDs have limited potential to decrease medication errors and increase efficiencies, but their impact is highly institution-specific, and use of this technology requires proper integration into an institution's medication distribution process. Before deploying

  9. Clinical decision making in dermatology: observation of consultations and the patients' perspectives.

    Science.gov (United States)

    Hajjaj, F M; Salek, M S; Basra, M K A; Finlay, A Y

    2010-01-01

    Clinical decision making is a complex process and might be influenced by a wide range of clinical and non-clinical factors. Little is known about this process in dermatology. The aim of this study was to explore the different types of management decisions made in dermatology and to identify factors influencing those decisions from observation of consultations and interviews with the patients. 61 patient consultations were observed by a physician with experience in dermatology. The patients were interviewed immediately after each consultation. Consultations and interviews were audio recorded, transcribed and their content analysed using thematic content analysis. The most common management decisions made during the consultations included: follow-up, carrying out laboratory investigation, starting new topical treatment, renewal of systemic treatment, renewal of topical treatment, discharging patients and starting new systemic treatment. Common influences on those decisions included: clinical factors such as ineffectiveness of previous therapy, adherence to prescribing guidelines, side-effects of medications, previous experience with the treatment, deterioration or improvement in the skin condition, and chronicity of skin condition. Non-clinical factors included: patient's quality of life, patient's friends or relatives, patient's time commitment, travel or transportation difficulties, treatment-related costs, availability of consultant, and availability of treatment. The study has shown that patients are aware that management decisions in dermatology are influenced by a wide range of clinical and non-clinical factors. Education programmes should be developed to improve the quality of decision making. Copyright © 2010 S. Karger AG, Basel.

  10. Implementation of workflow engine technology to deliver basic clinical decision support functionality

    Science.gov (United States)

    2011-01-01

    Background Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. Results We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. Conclusions We describe an implementation of

  11. Implementation of workflow engine technology to deliver basic clinical decision support functionality

    Directory of Open Access Journals (Sweden)

    Oberg Ryan

    2011-04-01

    Full Text Available Abstract Background Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. Results We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language process definition language (XPDL. The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent. We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. Conclusions We

  12. A controlled trial of automated classification of negation from clinical notes

    Directory of Open Access Journals (Sweden)

    Carruth William

    2005-05-01

    Full Text Available Abstract Background Identification of negation in electronic health records is essential if we are to understand the computable meaning of the records: Our objective is to compare the accuracy of an automated mechanism for assignment of Negation to clinical concepts within a compositional expression with Human Assigned Negation. Also to perform a failure analysis to identify the causes of poorly identified negation (i.e. Missed Conceptual Representation, Inaccurate Conceptual Representation, Missed Negation, Inaccurate identification of Negation. Methods 41 Clinical Documents (Medical Evaluations; sometimes outside of Mayo these are referred to as History and Physical Examinations were parsed using the Mayo Vocabulary Server Parsing Engine. SNOMED-CT™ was used to provide concept coverage for the clinical concepts in the record. These records resulted in identification of Concepts and textual clues to Negation. These records were reviewed by an independent medical terminologist, and the results were tallied in a spreadsheet. Where questions on the review arose Internal Medicine Faculty were employed to make a final determination. Results SNOMED-CT was used to provide concept coverage of the 14,792 Concepts in 41 Health Records from John's Hopkins University. Of these, 1,823 Concepts were identified as negative by Human review. The sensitivity (Recall of the assignment of negation was 97.2% (p Conclusion Automated assignment of negation to concepts identified in health records based on review of the text is feasible and practical. Lexical assignment of negation is a good test of true Negativity as judged by the high sensitivity, specificity and positive likelihood ratio of the test. SNOMED-CT had overall coverage of 88.7% of the concepts being negated.

  13. Automating annotation of information-giving for analysis of clinical conversation.

    Science.gov (United States)

    Mayfield, Elijah; Laws, M Barton; Wilson, Ira B; Penstein Rosé, Carolyn

    2014-02-01

    Coding of clinical communication for fine-grained features such as speech acts has produced a substantial literature. However, annotation by humans is laborious and expensive, limiting application of these methods. We aimed to show that through machine learning, computers could code certain categories of speech acts with sufficient reliability to make useful distinctions among clinical encounters. The data were transcripts of 415 routine outpatient visits of HIV patients which had previously been coded for speech acts using the Generalized Medical Interaction Analysis System (GMIAS); 50 had also been coded for larger scale features using the Comprehensive Analysis of the Structure of Encounters System (CASES). We aggregated selected speech acts into information-giving and requesting, then trained the machine to automatically annotate using logistic regression classification. We evaluated reliability by per-speech act accuracy. We used multiple regression to predict patient reports of communication quality from post-visit surveys using the patient and provider information-giving to information-requesting ratio (briefly, information-giving ratio) and patient gender. Automated coding produces moderate reliability with human coding (accuracy 71.2%, κ=0.57), with high correlation between machine and human prediction of the information-giving ratio (r=0.96). The regression significantly predicted four of five patient-reported measures of communication quality (r=0.263-0.344). The information-giving ratio is a useful and intuitive measure for predicting patient perception of provider-patient communication quality. These predictions can be made with automated annotation, which is a practical option for studying large collections of clinical encounters with objectivity, consistency, and low cost, providing greater opportunity for training and reflection for care providers.

  14. Complacency and Automation Bias in the Use of Imperfect Automation.

    Science.gov (United States)

    Wickens, Christopher D; Clegg, Benjamin A; Vieane, Alex Z; Sebok, Angelia L

    2015-08-01

    We examine the effects of two different kinds of decision-aiding automation errors on human-automation interaction (HAI), occurring at the first failure following repeated exposure to correctly functioning automation. The two errors are incorrect advice, triggering the automation bias, and missing advice, reflecting complacency. Contrasts between analogous automation errors in alerting systems, rather than decision aiding, have revealed that alerting false alarms are more problematic to HAI than alerting misses are. Prior research in decision aiding, although contrasting the two aiding errors (incorrect vs. missing), has confounded error expectancy. Participants performed an environmental process control simulation with and without decision aiding. For those with the aid, automation dependence was created through several trials of perfect aiding performance, and an unexpected automation error was then imposed in which automation was either gone (one group) or wrong (a second group). A control group received no automation support. The correct aid supported faster and more accurate diagnosis and lower workload. The aid failure degraded all three variables, but "automation wrong" had a much greater effect on accuracy, reflecting the automation bias, than did "automation gone," reflecting the impact of complacency. Some complacency was manifested for automation gone, by a longer latency and more modest reduction in accuracy. Automation wrong, creating the automation bias, appears to be a more problematic form of automation error than automation gone, reflecting complacency. Decision-aiding automation should indicate its lower degree of confidence in uncertain environments to avoid the automation bias. © 2015, Human Factors and Ergonomics Society.

  15. User-centered design to improve clinical decision support in primary care.

    Science.gov (United States)

    Brunner, Julian; Chuang, Emmeline; Goldzweig, Caroline; Cain, Cindy L; Sugar, Catherine; Yano, Elizabeth M

    2017-08-01

    A growing literature has demonstrated the ability of user-centered design to make clinical decision support systems more effective and easier to use. However, studies of user-centered design have rarely examined more than a handful of sites at a time, and have frequently neglected the implementation climate and organizational resources that influence clinical decision support. The inclusion of such factors was identified by a systematic review as "the most important improvement that can be made in health IT evaluations." (1) Identify the prevalence of four user-centered design practices at United States Veterans Affairs (VA) primary care clinics and assess the perceived utility of clinical decision support at those clinics; (2) Evaluate the association between those user-centered design practices and the perceived utility of clinical decision support. We analyzed clinic-level survey data collected in 2006-2007 from 170 VA primary care clinics. We examined four user-centered design practices: 1) pilot testing, 2) provider satisfaction assessment, 3) formal usability assessment, and 4) analysis of impact on performance improvement. We used a regression model to evaluate the association between user-centered design practices and the perceived utility of clinical decision support, while accounting for other important factors at those clinics, including implementation climate, available resources, and structural characteristics. We also examined associations separately at community-based clinics and at hospital-based clinics. User-centered design practices for clinical decision support varied across clinics: 74% conducted pilot testing, 62% conducted provider satisfaction assessment, 36% conducted a formal usability assessment, and 79% conducted an analysis of impact on performance improvement. Overall perceived utility of clinical decision support was high, with a mean rating of 4.17 (±.67) out of 5 on a composite measure. "Analysis of impact on performance

  16. Canine serum amyloid A (SAA) measured by automated latex agglutination turbidimetry is useful for routine sensitive and specific detection of systemic inflammation in a general clinical setting.

    Science.gov (United States)

    Christensen, Michelle B; Langhorn, Rebecca; Goddard, Amelia; Andreasen, Eva B; Moldal, Elena; Tvarijonaviciute, Asta; Kirpenteijn, Jolle; Jakobsen, Sabrina; Persson, Frida; Kjelgaard-Hansen, Mads

    2013-05-02

    Canine serum amyloid A (SAA) is a useful diagnostic marker of systemic inflammation. A latex agglutination turbidimetric immunoassay (LAT) was validated for automated measurements. The aim of the study was to evaluate the clinical applicability of SAA measured by the LAT. SAA was measured in 7 groups of dogs with and without systemic inflammation (n=247). Overlap performance was investigated. Diagnostic performance was compared to body temperature and leukocyte markers. Clinical decision limits for SAA were estimated. In dogs with neurological, neoplastic or gastrointestinal disorders (n=143), it was investigated whether a higher proportion of SAA positive dogs could be detected in cases of complications with risk of systemic inflammation. Significantly higher concentrations of SAA were measured in dogs with (range [48.75; 5,032 mg/l]), compared to dogs without systemic inflammation [0; 56.4 mg/l]. SAA was a more sensitive and specific marker of systemic inflammation (area under the receiver-operating characteristic curve (AUC) 1.00), compared to body temperature (0.6) and segmented neutrophils (best performing leukocyte marker, 0.84). A clinical decision limit of 56.4 mg/l was established giving close to perfect discrimination between dogs with and without systemic inflammation. Higher proportions of SAA-positive dogs were observed in dogs with neurological, neoplastic and gastrointestinal disorders with complications known to increase risk of systemic inflammation, compared to uncomplicated cases. The automated LAT makes SAA applicable as a relevant diagnostic marker of systemic inflammation in dogs for routine random-access real-time use in a general clinical setting.

  17. An automated standardized system for managing adverse events in clinical research networks.

    Science.gov (United States)

    Richesson, Rachel L; Malloy, Jamie F; Paulus, Kathleen; Cuthbertson, David; Krischer, Jeffrey P

    2008-01-01

    Multi-site clinical protocols and clinical research networks require tools to manage and monitor adverse events (AEs). To be successful, these tools must be designed to comply with applicable regulatory requirements, reflect current data standards, international directives and advances in pharmacovigilance, and be convenient and adaptable to multiple needs. We describe an Adverse Event Data Management System (AEDAMS) that is used across multiple study designs in the various clinical research networks and multi-site studies for which we provide data and technological support. Investigators enter AE data using a standardized and structured web-based data collection form. The automated AEDAMS forwards the AE information to individuals in designated roles (investigators, sponsors, Data Safety and Monitoring Boards) and manages subsequent communications in real time, as the entire reporting, review and notification is done by automatically generated emails. The system was designed to adhere to timelines and data requirements in compliance with Good Clinical Practice (International Conference on Harmonisation E6) reporting standards and US federal regulations, and can be configured to support AE management for many types of study designs and adhere to various domestic or international reporting requirements. This tool allows AEs to be collected in a standard way by multiple distributed users, facilitates accurate and timely AE reporting and reviews, and allows the centralized management of AEs. Our design justification and experience with the system are described.

  18. The professional medical ethics model of decision making under conditions of clinical uncertainty.

    Science.gov (United States)

    McCullough, Laurence B

    2013-02-01

    The professional medical ethics model of decision making may be applied to decisions clinicians and patients make under the conditions of clinical uncertainty that exist when evidence is low or very low. This model uses the ethical concepts of medicine as a profession, the professional virtues of integrity and candor and the patient's virtue of prudence, the moral management of medical uncertainty, and trial of intervention. These features combine to justifiably constrain clinicians' and patients' autonomy with the goal of preventing nondeliberative decisions of patients and clinicians. To prevent biased recommendations by the clinician that promote such nondeliberative decisions, medically reasonable alternatives supported by low or very low evidence should be offered but not recommended. The professional medical ethics model of decision making aims to improve the quality of decisions by reducing the unacceptable variation that can result from nondeliberative decision making by patients and clinicians when evidence is low or very low.

  19. Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach

    OpenAIRE

    Bennett, Casey C.; Hauser, Kris

    2013-01-01

    In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This serves two potential functions: 1) a simulation environment for expl...

  20. Strategies to facilitate shared decision-making about pediatric oncology clinical trial enrollment: A systematic review.

    Science.gov (United States)

    Robertson, Eden G; Wakefield, Claire E; Signorelli, Christina; Cohn, Richard J; Patenaude, Andrea; Foster, Claire; Pettit, Tristan; Fardell, Joanna E

    2018-02-11

    We conducted a systematic review to identify the strategies that have been recommended in the literature to facilitate shared decision-making regarding enrolment in pediatric oncology clinical trials. We searched seven databases for peer-reviewed literature, published 1990-2017. Of 924 articles identified, 17 studies were eligible for the review. We assessed study quality using the 'Mixed-Methods Appraisal Tool'. We coded the results and discussions of papers line-by-line using nVivo software. We categorized strategies thematically. Five main themes emerged: 1) decision-making as a process, 2) individuality of the process; 3) information provision, 4) the role of communication, or 5) decision and psychosocial support. Families should have adequate time to make a decision. HCPs should elicit parents' and patients' preferences for level of information and decision involvement. Information should be clear and provided in multiple modalities. Articles also recommended providing training for healthcare professionals and access to psychosocial support for families. High quality, individually-tailored information, open communication and psychosocial support appear vital in supporting decision-making regarding enrollment in clinical trials. These data will usefully inform future decision-making interventions/tools to support families making clinical trial decisions. A solid evidence-base for effective strategies which facilitate shared decision-making is needed. Copyright © 2018 Elsevier B.V. All rights reserved.

  1. Automated Analysis of Clinical Flow Cytometry Data: A Chronic Lymphocytic Leukemia Illustration.

    Science.gov (United States)

    Scheuermann, Richard H; Bui, Jack; Wang, Huan-You; Qian, Yu

    2017-12-01

    Flow cytometry is used in cell-based diagnostic evaluation for blood-borne malignancies including leukemia and lymphoma. The current practice for cytometry data analysis relies on manual gating to identify cell subsets in complex mixtures, which is subjective, labor-intensive, and poorly reproducible. This article reviews recent efforts to develop, validate, and disseminate automated computational methods and pipelines for cytometry data analysis that could help overcome the limitations of manual analysis and provide for efficient and data-driven diagnostic applications. It demonstrates the performance of an optimized computational pipeline in a pilot study of chronic lymphocytic leukemia data from the authors' clinical diagnostic laboratory. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Towards patient-centered colorectal cancer surgery : focus on risks, decisions and clinical auditing

    NARCIS (Netherlands)

    Snijders, Heleen Simone

    2014-01-01

    The aim of this thesis was to explore several aspects of both clinical decision making and quality assessment in colorectal cancer surgery. Part one focusses on benefits and risks of treatment options, preoperative information provision and Shared Decision Making (SDM); part two investigates changes

  3. mHealth for Clinical Decision-Making in Sub-Saharan Africa : A Scoping Review

    NARCIS (Netherlands)

    Adepoju, Ibukun-Oluwa Omolade; Albersen, Bregje Joanna Antonia; De Brouwere, Vincent; van Roosmalen, Jos; Zweekhorst, M.B.M.

    2017-01-01

    BACKGROUND: In a bid to deliver quality health services in resource-poor settings, mobile health (mHealth) is increasingly being adopted. The role of mHealth in facilitating evidence-based clinical decision-making through data collection, decision algorithms, and evidence-based guidelines, for

  4. Factors Predicting Oncology Care Providers' Behavioral Intention to Adopt Clinical Decision Support Systems

    Science.gov (United States)

    Wolfenden, Andrew

    2012-01-01

    The purpose of this quantitative correlation study was to examine the predictors of user behavioral intention on the decision of oncology care providers to adopt or reject the clinical decision support system. The Unified Theory of Acceptance and Use of Technology (UTAUT) formed the foundation of the research model and survey instrument. The…

  5. GRADE Guidelines: 16. GRADE evidence to decision frameworks for tests in clinical practice and public health

    NARCIS (Netherlands)

    Schünemann, Holger J.; Mustafa, Reem; Brozek, Jan; Santesso, Nancy; Alonso-Coello, Pablo; Guyatt, Gordon; Scholten, Rob; Langendam, Miranda; Leeflang, Mariska M.; Akl, Elie A.; Singh, Jasvinder A.; Meerpohl, Joerg; Hultcrantz, Monica; Bossuyt, Patrick; Oxman, Andrew D.; Singh, Jasvinder; Lange, Stefan; Parmelli, Elena; Moberg, Jenny; Rosenbaum, Sarah; Brignardello-Petersen, Romina; Wiercioch, Wojtek; Davoli, Marina; Nowak, Artur; Dietl, Bart

    2016-01-01

    Objectives: To describe the grading of recommendations assessment, development and evaluation (GRADE) interactive evidence to decision (EtD) frameworks for tests and test strategies for clinical, public health, or coverage decisions. Study Design and Setting: As part of the GRADE Working Group's

  6. GRADE Guidelines : 16. GRADE evidence to decision frameworks for tests in clinical practice and public health

    NARCIS (Netherlands)

    Schünemann, Holger J.; Mustafa, Reem A.; Brozek, Jan; Santesso, Nancy; Alonso-Coello, Pablo; Guyatt, Gordon; Scholten, Rob; Langendam, Miranda W; Leeflang, Mariska; Akl, Elie A.; Singh, Jasvinder A.; Meerpohl, Joerg; Hultcrantz, Monica; Bossuyt, Patrick Mm; Oxman, Andrew D.; Schünemann, Holger J.; Mustafa, Reem A.; Brozek, Jan; Santesso, Nancy; Alonso-Coello, Pablo; Scholten, Rob; Langendam, Miranda W; Bossuyt, Patrick Mm; Leeflang, Mariska; Singh, Jasvinder; Meerpohl, Joerg; Hultcrantz, Monica; Guyatt, Gordon; Oxman, Andrew D.; Lange, Stefan; Parmelli, Elena; Moberg, Jenny; Rosenbaum, Sarah; Brignardello-Petersen, Romina; Wiercioch, Wojtek; Davoli, Marina; Nowak, Artur; Dietl, Bart

    2016-01-01

    Objectives To describe the grading of recommendations assessment, development and evaluation (GRADE) interactive evidence to decision (EtD) frameworks for tests and test strategies for clinical, public health, or coverage decisions. Study Design and Setting As part of the GRADE Working Group's

  7. A statistical model for predicting the retrieval rate of separated instruments and clinical decision-making

    Directory of Open Access Journals (Sweden)

    Chen Lin

    2015-12-01

    Conclusion: A statistical model relating to root canal curvature and depth of separated instruments was established to evaluate the retrieval rate of separated instruments, and the result of this formulation may provide clues for clinical decision-making.

  8. Application of a diagnosis-based clinical decision guide in patients with neck pain

    OpenAIRE

    Murphy, Donald R; Hurwitz, Eric L

    2011-01-01

    Abstract Background Neck pain (NP) is a common cause of disability. Accurate and efficacious methods of diagnosis and treatment have been elusive. A diagnosis-based clinical decision guide (DBCDG; previously referred to as a diagnosis-based clinical decision rule) has been proposed which attempts to provide the clinician with a systematic, evidence-based guide in applying the biopsychosocial model of care. The approach is based on three questions of diagnosis. The purpose of this study is to ...

  9. Application of a diagnosis-based clinical decision guide in patients with low back pain

    OpenAIRE

    Murphy, Donald R; Hurwitz, Eric L

    2011-01-01

    Abstract Background Low back pain (LBP) is common and costly. Development of accurate and efficacious methods of diagnosis and treatment has been identified as a research priority. A diagnosis-based clinical decision guide (DBCDG; previously referred to as a diagnosis-based clinical decision rule) has been proposed which attempts to provide the clinician with a systematic, evidence-based means to apply the biopsychosocial model of care. The approach is based on three questions of diagnosis. T...

  10. Development and evaluation of learning module on clinical decision-making in Prosthodontics

    OpenAIRE

    Deshpande, Saee; Lambade, Dipti; Chahande, Jayashree

    2015-01-01

    Purpose: Best practice strategies for helping students learn the reasoning skills of problem solving and critical thinking (CT) remain a source of conjecture, particularly with regard to CT. The dental education literature is fundamentally devoid of research on the cognitive components of clinical decision-making. Aim: This study was aimed to develop and evaluate the impact of blended learning module on clinical decision-making skills of dental graduates for planning prosthodontics rehabilita...

  11. The role of emotion in clinical decision making: an integrative literature review

    OpenAIRE

    Kozlowski, Desirée; Hutchinson, Marie; Hurley, John; Rowley, Joanne; Sutherland, Joanna

    2017-01-01

    Background Traditionally, clinical decision making has been perceived as a purely rational and cognitive process. Recently, a number of authors have linked emotional intelligence (EI) to clinical decision making (CDM) and calls have been made for an increased focus on EI skills for clinicians. The objective of this integrative literature review was to identify and synthesise the empirical evidence for a role of emotion in CDM. Methods A systematic search of the bibliographic databases PubMed,...

  12. Accuracy of intuition in clinical decision-making among novice clinicians.

    Science.gov (United States)

    Price, Amanda; Zulkosky, Kristen; White, Krista; Pretz, Jean

    2017-05-01

    To assess the reliance on intuitive and analytical approaches during clinical decision-making among novice clinicians and whether that reliance is associated with accurate decision-making. Nurse educators and managers tend to emphasize analysis over intuition during clinical decision-making though nurses typically report some reliance on intuition in their practice. We hypothesized that under certain conditions, reliance on intuition would support accurate decision-making, even among novices. This study utilized an experimental design with clinical complication (familiar vs. novel) and decision phase (cue acquisition, diagnosis and action) as within-subjects' factors, and simulation role (observer, family, auxiliary nurse and primary nurse) as between-subjects' factor. We examined clinical decision-making accuracy among final semester pre-licensure nursing students in a simulation experience. Students recorded their reasoning about emerging clinical complications with their patient during two distinct points in the simulation; one point involved a familiar complication and the other a relatively novel complication. All data were collected during Spring 2015. Although most participants relied more heavily on analysis than on intuition, use of intuition during the familiar complication was associated with more accurate decision-making, particularly in guiding attention to relevant cues. With the novel complication, use of intuition appeared to hamper decision-making, particularly for those in an observer role. Novice clinicians should be supported by educators and nurse managers to note when their intuitions are likely to be valid. Our findings emphasize the integrated nature of intuition and analysis in clinical decision-making. © 2016 John Wiley & Sons Ltd.

  13. Development and evaluation of learning module on clinical decision-making in Prosthodontics

    Science.gov (United States)

    Deshpande, Saee; Lambade, Dipti; Chahande, Jayashree

    2015-01-01

    Purpose: Best practice strategies for helping students learn the reasoning skills of problem solving and critical thinking (CT) remain a source of conjecture, particularly with regard to CT. The dental education literature is fundamentally devoid of research on the cognitive components of clinical decision-making. Aim: This study was aimed to develop and evaluate the impact of blended learning module on clinical decision-making skills of dental graduates for planning prosthodontics rehabilitation. Methodology: An interactive teaching module consisting of didactic lectures on clinical decision-making and a computer-assisted case-based treatment planning software was developed Its impact on cognitive knowledge gain in clinical decision-making was evaluated using an assessment involving problem-based multiple choice questions and paper-based case scenarios. Results: Mean test scores were: Pretest (17 ± 1), posttest 1 (21 ± 2) and posttest 2 (43 ± 3). Comparison of mean scores was done with one-way ANOVA test. There was overall significant difference in between mean scores at all the three points (P posttest 1 > pretest. Conclusion: Blended teaching methods employing didactic lectures on the clinical decision-making as well as computer assisted case-based learning can be used to improve quality of clinical decision-making in prosthodontic rehabilitation for dental graduates. PMID:26929504

  14. Development and evaluation of learning module on clinical decision-making in Prosthodontics.

    Science.gov (United States)

    Deshpande, Saee; Lambade, Dipti; Chahande, Jayashree

    2015-01-01

    Best practice strategies for helping students learn the reasoning skills of problem solving and critical thinking (CT) remain a source of conjecture, particularly with regard to CT. The dental education literature is fundamentally devoid of research on the cognitive components of clinical decision-making. This study was aimed to develop and evaluate the impact of blended learning module on clinical decision-making skills of dental graduates for planning prosthodontics rehabilitation. An interactive teaching module consisting of didactic lectures on clinical decision-making and a computer-assisted case-based treatment planning software was developed Its impact on cognitive knowledge gain in clinical decision-making was evaluated using an assessment involving problem-based multiple choice questions and paper-based case scenarios. Mean test scores were: Pretest (17 ± 1), posttest 1 (21 ± 2) and posttest 2 (43 ± 3). Comparison of mean scores was done with one-way ANOVA test. There was overall significant difference in between mean scores at all the three points (P posttest 1 > pretest. Blended teaching methods employing didactic lectures on the clinical decision-making as well as computer assisted case-based learning can be used to improve quality of clinical decision-making in prosthodontic rehabilitation for dental graduates.

  15. The Effect of Algorithm-Based Learning on Clinical Decision Making Abilities of Medical Emergency Students

    Directory of Open Access Journals (Sweden)

    H Asayesh

    2015-12-01

    Full Text Available Introduction: Improvement of students’ clinical decision making is one of the main challenges in medical education. There are numerous ways to improve these skills. The aim of this study was to examine the effect of algorithm-based learning on clinical decision making abilities of medical emergency students. Method: in this experimental study, twenty five medical emergency students were randomly assigned to algorithm based learning group  (n=13 and control group  (n=12. Student in algorithm-based learning group were educated the diagnosis and treatment of selected medical emergency situation with algorithmic approach. Education in the control group was conducted by a routine lecture, along with copies of educational content. Three-hour training period was held for both groups  (two separate sessions with an interval day. After intervention, clinical decision making of the students in both group were measured by clinical scenarios and clinical decision making self-efficacy scale. Results: The mean of acquired scores from clinical scenarios among students in algorithm-based learning group was 17.50  (±1.67 and in the control group was 14.50 (±2.63. The differences was statistically significant  (t=0.006, P=0.006. The students in algorithm-based learning group had better scores in the clinical decision making in terms of self-efficacy scale and it was 13.30 (1.57 and in the control group this mean was 10.32  (3.05. In this case, the differences was statistically significant (t=3.01, P=0.009. Conclusion: algorithm-based learning is effective in improvement of clinical decision making and applying of this method along with other educational methods could promote students’ clinical decision making especially in medical emergency situations.

  16. Urinary orosomucoid: validation of an automated immune turbidimetric test and its possible clinical use.

    Science.gov (United States)

    Kustán, Péter; Szirmay, Balázs; Horváth-Szalai, Zoltán; Ludány, Andrea; Lakatos, Ágnes; Mühl, Diána; Christensen, Per Hjort; Miseta, Attila; Kovács, Gábor L; Kőszegi, Tamás

    2016-10-15

    Besides routine serum markers of inflammatory diseases, the diagnostic potential of selected urinary proteins has not been fully exploited yet. Former studies revealed that urinary orosomucoid (u-ORM) might have complementary information in inflammatory disorders. Our aim was to develop and validate a fully automated method for u-ORM measurements and to evaluate its potential clinical impact on systemic inflammatory diseases. A particle-enhanced immune turbidimetric assay was validated for a Cobas 8000/c502 analyzer to determine u-ORM levels. Spot urine samples from 72 healthy individuals, 28 patients with Crohn's disease and 30 septic patients were studied. Our assay time was 10 minutes and the detection limit of u-ORM was 0.02 mg/L. The intra- and inter-assay imprecision expressed as CV was less than 5%, and the recovery ranged between 95-103%. Within 10 to 60 years of age, a preliminary reference range for urinary orosomucoid/creatinine ratio (u-ORM/u-CREAT) was found to be 0.08 (0.01-0.24) mg/mmol [median (2.5-97.5 percentiles)]. Compared to controls, a five-fold increase of u-ORM/u-CREAT values in Crohn's disease and approximately a 240-fold increase in sepsis were observed. We set up a fast, sensitive and precise turbidimetric approach for automated u-ORM determination. Our highly sensitive assay is ideal for routine u-ORM measurements and might be a potential novel laboratory test in the management of systemic inflammatory processes.

  17. Evidence-Based Clinical Decision: Key to Improved Patients Care ...

    African Journals Online (AJOL)

    ... materials remain limited to mostly developed countries. There is need to adopt measures to further facilitate dissemination of current information of effective health to care providers and policymakers in resource-poor countries. This review is aimed at re-enforcing the need for applying best-evidence into clinical practice

  18. Surgical oncologic emergencies : Decision making and clinical outcome

    NARCIS (Netherlands)

    Bosscher, Marianne Roberta Frederiek

    2015-01-01

    Oncologic emergencies are acute, potentially life threatening conditions that have developed as a result of malignant disease or cancer treatment. The clinical outcome of patients with symptoms caused by malignant disease is often poor and short term mortality is high. In an emergency situation,

  19. A study to explore if dentists' anxiety affects their clinical decision-making.

    Science.gov (United States)

    Chipchase, S Y; Chapman, H R; Bretherton, R

    2017-02-24

    Aims To develop a measure of dentists' anxiety in clinical situations; to establish if dentists' anxiety in clinical situations affected their self-reported clinical decision-making; to establish if occupational stress, as demonstrated by burnout, is associated with anxiety in clinical situations and clinical decision-making; and to explore the relationship between decision-making style and the clinical decisions which are influenced by anxiety.Design Cross-sectional study.Setting Primary Dental Care.Subjects and methods A questionnaire battery [Maslach Burnout Inventory, measuring burnout; Melbourne Decision Making Questionnaire, measuring decision-making style; Dealing with Uncertainty Questionnaire (DUQ), measuring coping with diagnostic uncertainty; and a newly designed Dentists' Anxieties in Clinical Situations Scale, measuring dentists' anxiety (DACSS-R) and change of treatment (DACSS-C)] was distributed to dentists practicing in Nottinghamshire and Lincolnshire. Demographic data were collected and dentists gave examples of anxiety-provoking situations and their responses to them.Main outcome measure Respondents' self-reported anxiety in various clinical situations on a 11-point Likert Scale (DACSS-R) and self-reported changes in clinical procedures (Yes/No; DACSS-C). The DACSS was validated using multiple t-tests and a principal component analysis. Differences in DACSS-R ratings and burnout, decision-making and dealing with uncertainty were explored using Pearson correlations and multiple regression analysis. Qualitative data was subject to a thematic analysis.Results The DACSS-R revealed a four-factor structure and had high internal reliability (Cronbach's α = 0.94). Those with higher DACSS-R scores of anxiety were more likely to report changes in clinical procedures (DACSS-C scores). DACSS-R scores were associated with decision-making self-esteem and style as measured by the MDMQ and all burnout subscales, though not with scores on the DUQ scale

  20. Information management to enable personalized medicine: stakeholder roles in building clinical decision support

    Directory of Open Access Journals (Sweden)

    Brinner Kristin M

    2009-10-01

    Full Text Available Abstract Background Advances in technology and the scientific understanding of disease processes are presenting new opportunities to improve health through individualized approaches to patient management referred to as personalized medicine. Future health care strategies that deploy genomic technologies and molecular therapies will bring opportunities to prevent, predict, and pre-empt disease processes but will be dependent on knowledge management capabilities for health care providers that are not currently available. A key cornerstone to the potential application of this knowledge will be effective use of electronic health records. In particular, appropriate clinical use of genomic test results and molecularly-targeted therapies present important challenges in patient management that can be effectively addressed using electronic clinical decision support technologies. Discussion Approaches to shaping future health information needs for personalized medicine were undertaken by a work group of the American Health Information Community. A needs assessment for clinical decision support in electronic health record systems to support personalized medical practices was conducted to guide health future development activities. Further, a suggested action plan was developed for government, researchers and research institutions, developers of electronic information tools (including clinical guidelines, and quality measures, and standards development organizations to meet the needs for personalized approaches to medical practice. In this article, we focus these activities on stakeholder organizations as an operational framework to help identify and coordinate needs and opportunities for clinical decision support tools to enable personalized medicine. Summary This perspective addresses conceptual approaches that can be undertaken to develop and apply clinical decision support in electronic health record systems to achieve personalized medical care. In

  1. Information management to enable personalized medicine: stakeholder roles in building clinical decision support.

    Science.gov (United States)

    Downing, Gregory J; Boyle, Scott N; Brinner, Kristin M; Osheroff, Jerome A

    2009-10-08

    Advances in technology and the scientific understanding of disease processes are presenting new opportunities to improve health through individualized approaches to patient management referred to as personalized medicine. Future health care strategies that deploy genomic technologies and molecular therapies will bring opportunities to prevent, predict, and pre-empt disease processes but will be dependent on knowledge management capabilities for health care providers that are not currently available. A key cornerstone to the potential application of this knowledge will be effective use of electronic health records. In particular, appropriate clinical use of genomic test results and molecularly-targeted therapies present important challenges in patient management that can be effectively addressed using electronic clinical decision support technologies. Approaches to shaping future health information needs for personalized medicine were undertaken by a work group of the American Health Information Community. A needs assessment for clinical decision support in electronic health record systems to support personalized medical practices was conducted to guide health future development activities. Further, a suggested action plan was developed for government, researchers and research institutions, developers of electronic information tools (including clinical guidelines, and quality measures), and standards development organizations to meet the needs for personalized approaches to medical practice. In this article, we focus these activities on stakeholder organizations as an operational framework to help identify and coordinate needs and opportunities for clinical decision support tools to enable personalized medicine. This perspective addresses conceptual approaches that can be undertaken to develop and apply clinical decision support in electronic health record systems to achieve personalized medical care. In addition, to represent meaningful benefits to personalized

  2. [Knowledge management system for laboratory work and clinical decision support].

    Science.gov (United States)

    Inada, Masanori; Sato, Mayumi; Yoneyama, Akiko

    2011-05-01

    This paper discusses a knowledge management system for clinical laboratories. In the clinical laboratory of Toranomon Hospital, we receive about 20 questions relevant to laboratory tests per day from medical doctors or co-medical staff. These questions mostly involve the essence to appropriately accomplish laboratory tests. We have to answer them carefully and suitably because an incorrect answer may cause a medical accident. Up to now, no method has been in place to achieve a rapid response and standardized answers. For this reason, the laboratory staff have responded to various questions based on their individual knowledge. We began to develop a knowledge management system to promote the knowledge of staff working for the laboratory. This system is a type of knowledge base for assisting the work, such as inquiry management, laboratory consultation, process management, and clinical support. It consists of several functions: guiding laboratory test information, managing inquiries from medical staff, reporting results of patient consultation, distributing laboratory staffs notes, and recording guidelines for laboratory medicine. The laboratory test information guide has 2,000 records of medical test information registered in the database with flexible retrieval. The inquiry management tool provides a methos to record all questions, answer easily, and retrieve cases. It helps staff to respond appropriately in a short period of time. The consulting report system treats patients' claims regarding medical tests. The laboratory staffs notes enter a file management system so they can be accessed to aid in clinical support. Knowledge sharing using this function can achieve the transition from individual to organizational learning. Storing guidelines for laboratory medicine will support EBM. Finally, it is expected that this system will support intellectual activity concerning laboratory work and contribute to the practice of knowledge management for clinical work support.

  3. An integrated, ethically driven environmental model of clinical decision making in emergency settings.

    Science.gov (United States)

    Wolf, Lisa

    2013-02-01

    To explore the relationship between multiple variables within a model of critical thinking and moral reasoning. A quantitative descriptive correlational design using a purposive sample of 200 emergency nurses. Measured variables were accuracy in clinical decision-making, moral reasoning, perceived care environment, and demographics. Analysis was by bivariate correlation using Pearson's product-moment correlation coefficients, chi square and multiple linear regression analysis. The elements as identified in the integrated ethically-driven environmental model of clinical decision-making (IEDEM-CD) corrected depict moral reasoning and environment of care as factors significantly affecting accuracy in decision-making. The integrated, ethically driven environmental model of clinical decision making is a framework useful for predicting clinical decision making accuracy for emergency nurses in practice, with further implications in education, research and policy. A diagnostic and therapeutic framework for identifying and remediating individual and environmental challenges to accurate clinical decision making. © 2012, The Author. International Journal of Nursing Knowledge © 2012, NANDA International.

  4. Patient-Centered Decision Support: Formative Usability Evaluation of Integrated Clinical Decision Support With a Patient Decision Aid for Minor Head Injury in the Emergency Department.

    Science.gov (United States)

    Melnick, Edward R; Hess, Erik P; Guo, George; Breslin, Maggie; Lopez, Kevin; Pavlo, Anthony J; Abujarad, Fuad; Powsner, Seth M; Post, Lori A

    2017-05-19

    The Canadian Computed Tomography (CT) Head Rule, a clinical decision rule designed to safely reduce imaging in minor head injury, has been rigorously validated and implemented, and yet expected decreases in CT were unsuccessful. Recent work has identified empathic care as a key component in decreasing CT overuse. Health information technology can hinder the clinician-patient relationship. Patient-centered decision tools to support the clinician-patient relationship are needed to promote evidence-based decisions. Our objective is to formatively evaluate an electronic tool that not only helps clinicians at the bedside to determine the need for CT use based on the Canadian CT Head Rule but also promotes evidence-based conversations between patients and clinicians regarding patient-specific risk and patients' specific concerns. User-centered design with practice-based and participatory decision aid development was used to design, develop, and evaluate patient-centered decision support regarding CT use in minor head injury in the emergency department. User experience and user interface (UX/UI) development involved successive iterations with incremental refinement in 4 phases: (1) initial prototype development, (2) usability assessment, (3) field testing, and (4) beta testing. This qualitative approach involved input from patients, emergency care clinicians, health services researchers, designers, and clinical informaticists at every stage. The Concussion or Brain Bleed app is the product of 16 successive iterative revisions in accordance with UX/UI industry design standards. This useful and usable final product integrates clinical decision support with a patient decision aid. It promotes shared use by emergency clinicians and patients at the point of care within the emergency department context. This tablet computer app facilitates evidence-based conversations regarding CT in minor head injury. It is adaptable to individual clinician practice styles. The resultant tool

  5. External validation of clinical decision rules for children with wrist trauma.

    Science.gov (United States)

    Mulders, Marjolein A M; Walenkamp, Monique M J; Dubois, Bente F H; Slaar, Annelie; Goslings, J Carel; Schep, Niels W L

    2017-05-01

    Clinical decision rules help to avoid potentially unnecessary radiographs of the wrist, reduce waiting times and save costs. The primary aim of this study was to provide an overview of all existing non-validated clinical decision rules for wrist trauma in children and to externally validate these rules in a different cohort of patients. Secondarily, we aimed to compare the performance of these rules with the validated Amsterdam Pediatric Wrist Rules. We included all studies that proposed a clinical prediction or decision rule in children presenting at the emergency department with acute wrist trauma. We performed external validation within a cohort of 379 children. We also calculated the sensitivity, specificity, negative predictive value and positive predictive value of each decision rule. We included three clinical decision rules. The sensitivity and specificity of all clinical decision rules after external validation were between 94% and 99%, and 11% and 26%, respectively. After external validation 7% to 17% less radiographs would be ordered and 1.4% to 5.7% of all fractures would be missed. Compared to the Amsterdam Pediatric Wrist Rules only one of the three other rules had a higher sensitivity; however both the specificity and the reduction in requested radiographs were lower in the other three rules. The sensitivity of the three non-validated clinical decision rules is high. However the specificity and the reduction in number of requested radiographs are low. In contrast, the validated Amsterdam Pediatric Wrist Rules has an acceptable sensitivity and the greatest reduction in radiographs, at 22%, without missing any clinically relevant fractures.

  6. The value of participatory development to support antimicrobial stewardship with a clinical decision support system

    NARCIS (Netherlands)

    Beerlage-de Jong, Nienke; Wentzel, Jobke; Hendrix, Ron; van Gemert-Pijnen, Lisette

    2017-01-01

    Background: Current clinical decision support systems (CDSSs) for antimicrobial stewardship programs (ASPs) are guideline- or expert-driven. They are focused on (clinical) content, not on supporting real-time workflow. Thus, CDSSs fail to optimally support prudent antimicrobial prescribing in daily

  7. The value of participatory development to support antimicrobial stewardship with a clinical decision support system

    NARCIS (Netherlands)

    Beerlage-de Jong, Nienke; Wentzel, M.J.; Hendrix, Ron; van Gemert-Pijnen, Julia E.W.C.

    Background Current clinical decision support systems (CDSSs) for antimicrobial stewardship programs (ASPs) are guideline- or expert-driven. They are focused on (clinical) content, not on supporting real-time workflow. Thus, CDSSs fail to optimally support prudent antimicrobial prescribing in daily

  8. LERM (Logical Elements Rule Method): A method for assessing and formalizing clinical rules for decision support

    NARCIS (Netherlands)

    Medlock, Stephanie; Opondo, Dedan; Eslami, Saeid; Askari, Marjan; Wierenga, Peter; de Rooij, Sophia E.; Abu-Hanna, Ameen

    2011-01-01

    Purpose: The aim of this study was to create a step-by-step method for transforming clinical rules for use in decision support, and to validate this method for usability and reliability. Methods: A sample set of clinical rules was identified from the relevant literature. Using an iterative approach

  9. Knowledge bases, clinical decision support systems, and rapid learning in oncology.

    Science.gov (United States)

    Yu, Peter Paul

    2015-03-01

    One of the most important benefits of health information technology is to assist the cognitive process of the human mind in the face of vast amounts of health data, limited time for decision making, and the complexity of the patient with cancer. Clinical decision support tools are frequently cited as a technologic solution to this problem, but to date useful clinical decision support systems (CDSS) have been limited in utility and implementation. This article describes three unique sources of health data that underlie fundamentally different types of knowledge bases which feed into CDSS. CDSS themselves comprise a variety of models which are discussed. The relationship of knowledge bases and CDSS to rapid learning health systems design is critical as CDSS are essential drivers of rapid learning in clinical care. Copyright © 2015 by American Society of Clinical Oncology.

  10. Automation in an Addiction Treatment Research Clinic: Computerized Contingency Management, Ecological Momentary Assessment, and a Protocol Workflow System

    Science.gov (United States)

    Vahabzadeh, Massoud; Lin, Jia-Ling; Mezghanni, Mustapha; Epstein, David H.; Preston, Kenzie L.

    2009-01-01

    Issues A challenge in treatment research is the necessity of adhering to protocol and regulatory strictures while maintaining flexibility to meet patients’ treatment needs and accommodate variations among protocols. Another challenge is the acquisition of large amounts of data in an occasionally hectic environment, along with provision of seamless methods for exporting, mining, and querying the data. Approach We have automated several major functions of our outpatient treatment research clinic for studies in drug abuse and dependence. Here we describe three such specialized applications: the Automated Contingency Management (ACM) system for delivery of behavioral interventions, the Transactional Electronic Diary (TED) system for management of behavioral assessments, and the Protocol Workflow System (PWS) for computerized workflow automation and guidance of each participant’s daily clinic activities. These modules are integrated into our larger information system to enable data sharing in real time among authorized staff. Key Findings ACM and TED have each permitted us to conduct research that was not previously possible. In addition, the time to data analysis at the end of each study is substantially shorter. With the implementation of the PWS, we have been able to manage a research clinic with an 80-patient capacity having an annual average of 18,000 patient-visits and 7,300 urine collections with a research staff of five. Finally, automated data management has considerably enhanced our ability to monitor and summarize participant-safety data for research oversight. Implications and conclusion When developed in consultation with end users, automation in treatment-research clinics can enable more efficient operations, better communication among staff, and expansions in research methods. PMID:19320669

  11. SU-G-TeP1-05: Development and Clinical Introduction of Automated Radiotherapy Treatment Planning for Prostate Cancer

    International Nuclear Information System (INIS)

    Winkel, D; Bol, GH; Asselen, B van; Hes, J; Scholten, V; Kerkmeijer, LGW; Raaymakers, BW

    2016-01-01

    Purpose: To develop an automated radiotherapy treatment planning and optimization workflow for prostate cancer in order to generate clinical treatment plans. Methods: A fully automated radiotherapy treatment planning and optimization workflow was developed based on the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). To evaluate our method, a retrospective planning study (n=100) was performed on patients treated for prostate cancer with 5 field intensity modulated radiotherapy, receiving a dose of 35×2Gy to the prostate and vesicles and a simultaneous integrated boost of 35×0.2Gy to the prostate only. A comparison was made between the dosimetric values of the automatically and manually generated plans. Operator time to generate a plan and plan efficiency was measured. Results: A comparison of the dosimetric values show that automatically generated plans yield more beneficial dosimetric values. In automatic plans reductions of 43% in the V72Gy of the rectum and 13% in the V72Gy of the bladder are observed when compared to the manually generated plans. Smaller variance in dosimetric values is seen, i.e. the intra- and interplanner variability is decreased. For 97% of the automatically generated plans and 86% of the clinical plans all criteria for target coverage and organs at risk constraints are met. The amount of plan segments and monitor units is reduced by 13% and 9% respectively. Automated planning requires less than one minute of operator time compared to over an hour for manual planning. Conclusion: The automatically generated plans are highly suitable for clinical use. The plans have less variance and a large gain in time efficiency has been achieved. Currently, a pilot study is performed, comparing the preference of the clinician and clinical physicist for the automatic versus manual plan. Future work will include expanding our automated treatment planning method to other tumor sites and develop other automated radiotherapy workflows.

  12. MODULAR ANALYTICS: A New Approach to Automation in the Clinical Laboratory.

    Science.gov (United States)

    Horowitz, Gary L; Zaman, Zahur; Blanckaert, Norbert J C; Chan, Daniel W; Dubois, Jeffrey A; Golaz, Olivier; Mensi, Noury; Keller, Franz; Stolz, Herbert; Klingler, Karl; Marocchi, Alessandro; Prencipe, Lorenzo; McLawhon, Ronald W; Nilsen, Olaug L; Oellerich, Michael; Luthe, Hilmar; Orsonneau, Jean-Luc; Richeux, Gérard; Recio, Fernando; Roldan, Esther; Rymo, Lars; Wicktorsson, Anne-Charlotte; Welch, Shirley L; Wieland, Heinrich; Grawitz, Andrea Busse; Mitsumaki, Hiroshi; McGovern, Margaret; Ng, Katherine; Stockmann, Wolfgang

    2005-01-01

    MODULAR ANALYTICS (Roche Diagnostics) (MODULAR ANALYTICS, Elecsys and Cobas Integra are trademarks of a member of the Roche Group) represents a new approach to automation for the clinical chemistry laboratory. It consists of a control unit, a core unit with a bidirectional multitrack rack transportation system, and three distinct kinds of analytical modules: an ISE module, a P800 module (44 photometric tests, throughput of up to 800 tests/h), and a D2400 module (16 photometric tests, throughput up to 2400 tests/h). MODULAR ANALYTICS allows customised configurations for various laboratory workloads. The performance and practicability of MODULAR ANALYTICS were evaluated in an international multicentre study at 16 sites. Studies included precision, accuracy, analytical range, carry-over, and workflow assessment. More than 700 000 results were obtained during the course of the study. Median between-day CVs were typically less than 3% for clinical chemistries and less than 6% for homogeneous immunoassays. Median recoveries for nearly all standardised reference materials were within 5% of assigned values. Method comparisons versus current existing routine instrumentation were clinically acceptable in all cases. During the workflow studies, the work from three to four single workstations was transferred to MODULAR ANALYTICS, which offered over 100 possible methods, with reduction in sample splitting, handling errors, and turnaround time. Typical sample processing time on MODULAR ANALYTICS was less than 30 minutes, an improvement from the current laboratory systems. By combining multiple analytic units in flexible ways, MODULAR ANALYTICS met diverse laboratory needs and offered improvement in workflow over current laboratory situations. It increased overall efficiency while maintaining (or improving) quality.

  13. Drug susceptibility testing of clinical isolates of streptococci and enterococci by the Phoenix automated microbiology system

    Directory of Open Access Journals (Sweden)

    Sokeng Gertrude

    2007-05-01

    Full Text Available Abstract Background Drug resistance is an emerging problem among streptococcal and enterococcal species. Automated diagnostic systems for species identification and antimicrobial susceptibility testing (AST have become recently available. We evaluated drug susceptibility of clinical isolates of streptococci and enterococci using the recent Phoenix system (BD, Sparks, MD. Diagnostic tools included the new SMIC/ID-2 panel for streptococci, and the PMIC/ID-14 for enterococci. Two-hundred and fifty isolates have been investigated: β-hemolytic streptococci (n = 65, Streptococcus pneumoniae (n = 50, viridans group streptococci (n = 32, Enterococcus faecium (n = 40, Enterococcus faecalis (n = 43, other catalase-negative cocci (n = 20. When needed, species ID was determined using molecular methods. Test bacterial strains were chosen among those carrying clinically-relevant resistance determinants (penicillin, macrolides, fluoroquinolones, glycopeptides. AST results of the Phoenix system were compared to minimal inhibitory concentration (MIC values measured by the Etest method (AB Biodisk, Solna, Sweden. Results Streptococci: essential agreement (EA and categorical agreement (CA were 91.9% and 98.8%, respectively. Major (ME and minor errors (mE accounted for 0.1% and 1.1% of isolates, respectively. No very major errors (VME were produced. Enterococci: EA was 97%, CA 96%. Small numbers of VME (0.9%, ME (1.4% and mE (2.8% were obtained. Overall, EA and CA rates for most drugs were above 90% for both genera. A few VME were found: a teicoplanin and high-level streptomycin for E. faecalis, b high-level gentamicin for E. faecium. The mean time to results (± SD was 11.8 ± 0.9 h, with minor differences between streptococci and enterococci. Conclusion The Phoenix system emerged as an effective tool for quantitative AST. Panels based on dilution tests provided rapid and accurate MIC values with regard to clinically-relevant streptococcal and enterococcal

  14. Eosinophilia detected by automated blood cell counting in ambulatory North American outpatients. Incidence and clinical significance.

    Science.gov (United States)

    Brigden, M; Graydon, C

    1997-09-01

    To audit a cohort of ambulatory outpatients with eosinophilia detected on automated blood cell counting. Specific objectives included the determination of whether the eosinophilia had been anticipated, the etiology of the eosinophilia, the clinical follow-up and investigations performed on patients with eosinophilia, and the effect of the detection of eosinophilia on patient management and ultimate clinical outcome. A year-long retrospective review of all patients with an absolute eosinophil count of greater than 0.7 x 10(9)/L. A large outpatient laboratory system. The patient population was managed by family physicians and specialists. Data collection included the results of the hematology profile, the absolute eosinophil count, the clinical situation responsible for the hematologic profile determination, and the probable cause of eosinophilia. Individual physicians were surveyed to determine if discovery of the eosinophilia had changed patient management plan or clinical outcome. Out of 195,300 patients who had a hematology profile performed, 225 were found to have an absolute eosinophilia count higher than 0.7 x 10(9)/L. The overall incidence of eosinophilia in the study population was 0.1%. The eosinophilia was not anticipated in 85% of patients. No obvious cause was detected for the eosinophilia in 36% of patients. Various allergic diseases were responsible for the eosinophilia in the majority of the remaining patients. Fewer than 9% of individuals manifested a serious systemic illness or parasitemia. Further clinical follow-up had been performed in 69% of patients. Additional laboratory tests had been ordered in 59% of patients. The laboratory tests most frequently ordered were a repeat hematology profile or stool examinations for ova and parasites. In only two instances did the discovery of the eosinophilia appear to result in a significant change in patient management or ultimate clinical income. The vast majority of eosinophilias detected in ambulatory

  15. Quantitative ultrasound texture analysis for clinical decision making support

    Science.gov (United States)

    Wu, Jie Ying; Beland, Michael; Konrad, Joseph; Tuomi, Adam; Glidden, David; Grand, David; Merck, Derek

    2015-03-01

    We propose a general ultrasound (US) texture-analysis and machine-learning framework for detecting the presence of disease that is suitable for clinical application across clinicians, disease types, devices, and operators. Its stages are image selection, image filtering, ROI selection, feature parameterization, and classification. Each stage is modular and can be replaced with alternate methods. Thus, this framework is adaptable to a wide range of tasks. Our two preliminary clinical targets are hepatic steatosis and adenomyosis diagnosis. For steatosis, we collected US images from 288 patients and their pathology-determined values of steatosis (%) from biopsies. Two radiologists independently reviewed all images and identified the region of interest (ROI) most representative of the hepatic echotexture for each patient. To parameterize the images into comparable quantities, we filter the US images at multiple scales for various texture responses. For each response, we collect a histogram of pixel features within the ROI, and parameterize it as a Gaussian function using its mean, standard deviation, kurtosis, and skew to create a 36-feature vector. Our algorithm uses a support vector machine (SVM) for classification. Using a threshold of 10%, we achieved 72.81% overall accuracy, 76.18% sensitivity, and 65.96% specificity in identifying steatosis with leave-ten-out cross-validation (p<0.0001). Extending this framework to adenomyosis, we identified 38 patients with MR-confirmed findings of adenomyosis and previous US studies and 50 controls. A single rater picked the best US-image and ROI for each case. Using the same processing pipeline, we obtained 76.14% accuracy, 86.00% sensitivity, and 63.16% specificity with leave-one-out cross-validation (p<0.0001).

  16. Automated electrocardiogram interpretation programs versus cardiologists' triage decision making based on teletransmitted data in patients with suspected acute coronary syndrome

    DEFF Research Database (Denmark)

    Clark, Elaine N; Ripa, Maria Sejersten; Clemmensen, Peter

    2010-01-01

    and to assess the effectiveness of cardiologists' triage decisions for these patients based on initial electrocardiogram. Twelve-lead electrocardiograms were recorded in ambulances using a LIFEPAK 12 monitor/defibrillator (Physio-Control, Inc., Redmond, Washington) and transmitted digitally to an attending.......02) and the cardiologists (p = 0.004). Triage decisions were effective, with good agreement between cardiologists' decisions and discharge diagnoses....

  17. Clinical judgment and decision-making in wound assessment and management: is experience enough?

    Science.gov (United States)

    Logan, Gemma

    2015-03-01

    The assessment and management of wounds forms a large proportion of community nurses' workload, often requiring judgment and decision-making in complex, challenging and uncertain circumstances. The processes through which nurses form judgments and make decisions within this context are reviewed in this article against existing theories on these subjects. There is variability in wound assessment and management practice which may be attributed to uncertainties within the context, a lack of knowledge in appropriate treatment choices and the inability to correctly value the importance of the clinical information presented. Nurses may be required to draw on intuition to guide their judgments and decision-making by association with experience and expertise. In addition, a step-by-step analytical approach underpinned by an evidence base may be required to ensure accuracy in practice. Developing an understanding of the different theories of judgment and decision-making may facilitate nurses' abilities to reflect on their own decision tasks, thereby enhancing the care provided.

  18. Automation bias: empirical results assessing influencing factors.

    Science.gov (United States)

    Goddard, Kate; Roudsari, Abdul; Wyatt, Jeremy C

    2014-05-01

    To investigate the rate of automation bias - the propensity of people to over rely on automated advice and the factors associated with it. Tested factors were attitudinal - trust and confidence, non-attitudinal - decision support experience and clinical experience, and environmental - task difficulty. The paradigm of simulated decision support advice within a prescribing context was used. The study employed within participant before-after design, whereby 26 UK NHS General Practitioners were shown 20 hypothetical prescribing scenarios with prevalidated correct and incorrect answers - advice was incorrect in 6 scenarios. They were asked to prescribe for each case, followed by being shown simulated advice. Participants were then asked whether they wished to change their prescription, and the post-advice prescription was recorded. Rate of overall decision switching was captured. Automation bias was measured by negative consultations - correct to incorrect prescription switching. Participants changed prescriptions in 22.5% of scenarios. The pre-advice accuracy rate of the clinicians was 50.38%, which improved to 58.27% post-advice. The CDSS improved the decision accuracy in 13.1% of prescribing cases. The rate of automation bias, as measured by decision switches from correct pre-advice, to incorrect post-advice was 5.2% of all cases - a net improvement of 8%. More immediate factors such as trust in the specific CDSS, decision confidence, and task difficulty influenced rate of decision switching. Lower clinical experience was associated with more decision switching. Age, DSS experience and trust in CDSS generally were not significantly associated with decision switching. This study adds to the literature surrounding automation bias in terms of its potential frequency and influencing factors. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  19. Towards automated detection, semi-quantification and identification of microbial growth in clinical bacteriology: A proof of concept.

    Science.gov (United States)

    Croxatto, Antony; Marcelpoil, Raphaël; Orny, Cédrick; Morel, Didier; Prod'hom, Guy; Greub, Gilbert

    2017-12-01

    Automation in microbiology laboratories impacts management, workflow, productivity and quality. Further improvements will be driven by the development of intelligent image analysis allowing automated detection of microbial growth, release of sterile samples, identification and quantification of bacterial colonies and reading of AST disk diffusion assays. We investigated the potential benefit of intelligent imaging analysis by developing algorithms allowing automated detection, semi-quantification and identification of bacterial colonies. Defined monomicrobial and clinical urine samples were inoculated by the BD Kiestra™ InoqulA™ BT module. Image acquisition of plates was performed with the BD Kiestra™ ImagA BT digital imaging module using the BD Kiestra™ Optis™ imaging software. The algorithms were developed and trained using defined data sets and their performance evaluated on both defined and clinical samples. The detection algorithms exhibited 97.1% sensitivity and 93.6% specificity for microbial growth detection. Moreover, quantification accuracy of 80.2% and of 98.6% when accepting a 1 log tolerance was obtained with both defined monomicrobial and clinical urine samples, despite the presence of multiple species in the clinical samples. Automated identification accuracy of microbial colonies growing on chromogenic agar from defined isolates or clinical urine samples ranged from 98.3% to 99.7%, depending on the bacterial species tested. The development of intelligent algorithm represents a major innovation that has the potential to significantly increase laboratory quality and productivity while reducing turn-around-times. Further development and validation with larger numbers of defined and clinical samples should be performed before transferring intelligent imaging analysis into diagnostic laboratories. Copyright © 2017 Chang Gung University. Published by Elsevier B.V. All rights reserved.

  20. Partitioning knowledge bases between advanced notification and clinical decision support systems.

    Science.gov (United States)

    Borlawsky, Tara; Li, Jianrong; Jalan, Srikant; Stern, Edie; Williams, Rose; Lussier, Yves A

    2005-01-01

    Due to the varying rates of change of ephemeral administrative and enduring clinical knowledge in decision support systems (DSSs), the functional partition of knowledge base (KB) components can lead to more efficient and cost-effective system implementation and maintenance. Our prototype loosely couples a clinical event monitor developed by Columbia University Medical Center (CUMC) with a secure notification service proxy developed by IBM Research to form a novel and complex clinical event communication service.

  1. A social-technological epistemology of clinical decision-making as mediated by imaging.

    Science.gov (United States)

    van Baalen, Sophie; Carusi, Annamaria; Sabroe, Ian; Kiely, David G

    2017-10-01

    In recent years there has been growing attention to the epistemology of clinical decision-making, but most studies have taken the individual physicians as the central object of analysis. In this paper we argue that knowing in current medical practice has an inherently social character and that imaging plays a mediating role in these practices. We have analyzed clinical decision-making within a medical expert team involved in diagnosis and treatment of patients with pulmonary hypertension (PH), a rare disease requiring multidisciplinary team involvement in diagnosis and management. Within our field study, we conducted observations, interviews, video tasks, and a panel discussion. Decision-making in the PH clinic involves combining evidence from heterogeneous sources into a cohesive framing of a patient, in which interpretations of the different sources can be made consistent with each other. Because pieces of evidence are generated by people with different expertise and interpretation and adjustments take place in interaction between different experts, we argue that this process is socially distributed. Multidisciplinary team meetings are an important place where information is shared, discussed, interpreted, and adjusted, allowing for a collective way of seeing and a shared language to be developed. We demonstrate this with an example of image processing in the PH service, an instance in which knowledge is distributed over multiple people who play a crucial role in generating an evaluation of right heart function. Finally, we argue that images fulfill a mediating role in distributed knowing in 3 ways: first, as enablers or tools in acquiring information; second, as communication facilitators; and third, as pervasively framing the epistemic domain. With this study of clinical decision-making in diagnosis and treatment of PH, we have shown that clinical decision-making is highly social and mediated by technologies. The epistemology of clinical decision-making needs

  2. Autoscope: automated otoscopy image analysis to diagnose ear pathology and use of clinically motivated eardrum features

    Science.gov (United States)

    Senaras, Caglar; Moberly, Aaron C.; Teknos, Theodoros; Essig, Garth; Elmaraghy, Charles; Taj-Schaal, Nazhat; Yu, Lianbo; Gurcan, Metin

    2017-03-01

    In this study, we propose an automated otoscopy image analysis system called Autoscope. To the best of our knowledge, Autoscope is the first system designed to detect a wide range of eardrum abnormalities by using high-resolution otoscope images and report the condition of the eardrum as "normal" or "abnormal." In order to achieve this goal, first, we developed a preprocessing step to reduce camera-specific problems, detect the region of interest in the image, and prepare the image for further analysis. Subsequently, we designed a new set of clinically motivated eardrum features (CMEF). Furthermore, we evaluated the potential of the visual MPEG-7 descriptors for the task of tympanic membrane image classification. Then, we fused the information extracted from the CMEF and state-of-the-art computer vision features (CVF), which included MPEG-7 descriptors and two additional features together, using a state of the art classifier. In our experiments, 247 tympanic membrane images with 14 different types of abnormality were used, and Autoscope was able to classify the given tympanic membrane images as normal or abnormal with 84.6% accuracy.

  3. Automated multiple flow-injection analysis in clinical chemistry: determination of total protein with Biuret reagent.

    Science.gov (United States)

    Shideler, C E; Stewart, K K; Crump, J; Wills, M R; Savory, J; Renoe, B W

    1980-09-01

    We have examined the feasibility of the automated multiple flow-injection technique for application to clinical chemistry by adapting to this system the biuret method for the determination of total protein. Samples were discretely and rapidly introduced into a continuously flowing, nonsegmented reagent stream by means of an automatic sampler and high-pressure injection valve. Pumps operating at 1380-2070 kPa (200-300 psi) were utilized to introduce the biuret reagent and saline diluent into the system separately at flow rates of 72 and 47 microL/s, respectively. Use of 20-microL sample and a 3.0-s reaction-delay coil was adequately sensitive for analysis for total protein by this method. Samples were analyzed at a rate of 150/h with no detectable between-sample carryover. Within-run precision studies yielded relative standard deviations of 2.5% and less. Total protein values obtained by this method correlated well with those obtained by centrifugal analyzer and bubble-segmented continuous-flow biuret methods.

  4. Automated percutaneous lumbar discectomy: technique, indications and clinical follow-up in over 1000 patients

    Energy Technology Data Exchange (ETDEWEB)

    Bonaldi, G. [Department of Neuroradiology, Ospedali Riuniti, Bergamo (Italy)

    2003-10-01

    This paper summarises my experience, over 14 years, treating over 1350 patients suffering from lumbar disc pathology, using minimally invasive intradiscal decompressive percutaneous techniques. The vast majority underwent the method introduced by Onik in 1985, referred to as ''automated'' since it involves a mechanical probe, working by a ''suction and cutting'' action for removal of the nucleus pulposus. Postoperative follow-up of at least 6 months was available for 1047 patients aged 15-92 years, who underwent this procedure up to June 2002. Results, based on a patient satisfaction, have been good in 58% of patients at 2 months and in 67.5% at 6 months; they have been particularly favourable in some subgroups such as elderly people (79.5% of excellent or good results), patients previously operated upon (78%) and those with ''discogenic'' low back pain (79%). Complication rates have been extremely low (less than 1%) and all complications cleared up without sequelae. In comparison with other percutaneous disc treatments, Onik's achieves the best compromise between clinical efficacy, comfort for the patient and low invasiveness. (orig.)

  5. Implementing an integrative multi-agent clinical decision support system with open source software.

    Science.gov (United States)

    Sayyad Shirabad, Jelber; Wilk, Szymon; Michalowski, Wojtek; Farion, Ken

    2012-02-01

    Clinical decision making is a complex multi-stage process. Decision support can play an important role at each stage of this process. At present, the majority of clinical decision support systems have been focused on supporting only certain stages. In this paper we present the design and implementation of MET3-a prototype multi-agent system providing an integrative decision support that spans over the entire decision making process. The system helps physicians with data collection, diagnosis formulation, treatment planning and finding supporting evidence. MET3 integrates with external hospital information systems via HL7 messages and runs on various computing platforms available at the point of care (e.g., tablet computers, mobile phones). Building MET3 required sophisticated and reliable software technologies. In the past decade the open source software movement has produced mature, stable, industrial strength software systems with a large user base. Therefore, one of the decisions that should be considered before developing or acquiring a decision support system is whether or not one could use open source technologies instead of proprietary ones. We believe MET3 shows that the answer to this question is positive.

  6. Clinical decision making on the use of physical restraint in intensive care units

    Directory of Open Access Journals (Sweden)

    Xinqian Li

    2014-12-01

    Full Text Available Physical restraint is a common nursing intervention in intensive care units and nurses often use it to ensure patients' safety and to prevent unexpected accidents. However, existing literature indicated that the use of physical restraint is a complex one because of inadequate rationales, the negative physical and emotional effects on patients, but the lack of perceived alternatives. This paper is aimed to interpret the clinical decision-making theories related to the use of physical restraint in intensive care units in order to facilitate our understanding on the use of physical restraint and to evaluate the quality of decisions made by nurses. By reviewing the literature, intuition and heuristics are the main decision-making strategies related to the use of physical restraint in intensive care units because the rapid and reflexive nature of intuition and heuristics allow nurses to have a rapid response to urgent and emergent cases. However, it is problematic if nurses simply count their decision-making on experience rather than incorporate research evidence into clinical practice because of inadequate evidence to support the use of physical restraint. Besides that, such a rapid response may lead nurses to make decisions without adequate assessment and thinking and therefore biases and errors may be generated. Therefore, despite the importance of intuition and heuristics in decision-making in acute settings on the use of physical restraint, it is recommended that nurses should incorporate research evidence with their experience to make decisions and adequate assessment before implementing physical restraint is also necessary.

  7. Clinical Decision Making and Mental Health Service Use Among Persons With Severe Mental Illness Across Europe.

    Science.gov (United States)

    Cosh, Suzanne; Zenter, Nadja; Ay, Esra-Sultan; Loos, Sabine; Slade, Mike; De Rosa, Corrado; Luciano, Mario; Berecz, Roland; Glaub, Theodora; Munk-Jørgensen, Povl; Krogsgaard Bording, Malene; Rössler, Wulf; Kawohl, Wolfram; Puschner, Bernd

    2017-09-01

    The study explored relationships between preferences for and experiences of clinical decision making (CDM) with service use among persons with severe mental illness. Data from a prospective observational study in six European countries were examined. Associations of baseline staff-rated (N=213) and patient-rated (N=588) preferred and experienced decision making with service use were examined at baseline by using binomial regressions and at 12-month follow-up by using multilevel models. A preference by patients and staff for active patient involvement in decision making, rather than shared or passive decision making, was associated with longer hospital admissions and higher costs at baseline and with increases in admissions over 12 months (p=.043). Low patient-rated satisfaction with an experienced clinical decision was also related to increased costs over the study period (p=.005). A preference for shared decision making may reduce health care costs by reducing inpatient admissions. Patient satisfaction with decisions was a predictor of costs, and clinicians should maximize patient satisfaction with CDM.

  8. A pilot study of distributed knowledge management and clinical decision support in the cloud.

    Science.gov (United States)

    Dixon, Brian E; Simonaitis, Linas; Goldberg, Howard S; Paterno, Marilyn D; Schaeffer, Molly; Hongsermeier, Tonya; Wright, Adam; Middleton, Blackford

    2013-09-01

    Implement and perform pilot testing of web-based clinical decision support services using a novel framework for creating and managing clinical knowledge in a distributed fashion using the cloud. The pilot sought to (1) develop and test connectivity to an external clinical decision support (CDS) service, (2) assess the exchange of data to and knowledge from the external CDS service, and (3) capture lessons to guide expansion to more practice sites and users. The Clinical Decision Support Consortium created a repository of shared CDS knowledge for managing hypertension, diabetes, and coronary artery disease in a community cloud hosted by Partners HealthCare. A limited data set for primary care patients at a separate health system was securely transmitted to a CDS rules engine hosted in the cloud. Preventive care reminders triggered by the limited data set were returned for display to clinician end users for review and display. During a pilot study, we (1) monitored connectivity and system performance, (2) studied the exchange of data and decision support reminders between the two health systems, and (3) captured lessons. During the six month pilot study, there were 1339 patient encounters in which information was successfully exchanged. Preventive care reminders were displayed during 57% of patient visits, most often reminding physicians to monitor blood pressure for hypertensive patients (29%) and order eye exams for patients with diabetes (28%). Lessons learned were grouped into five themes: performance, governance, semantic interoperability, ongoing adjustments, and usability. Remote, asynchronous cloud-based decision support performed reasonably well, although issues concerning governance, semantic interoperability, and usability remain key challenges for successful adoption and use of cloud-based CDS that will require collaboration between biomedical informatics and computer science disciplines. Decision support in the cloud is feasible and may be a reasonable

  9. Potential Role of Methylation Marker in Glioma Supporting Clinical Decisions

    Directory of Open Access Journals (Sweden)

    Krzysztof Roszkowski

    2016-11-01

    Full Text Available The IDH1/2 gene mutations, ATRX loss/mutation, 1p/19q status, and MGMT promoter methylation are increasingly used as prognostic or predictive biomarkers of gliomas. However, the effect of their combination on radiation therapy outcome is discussable. Previously, we demonstrated that the IDH1 c.G395A; p.R132H mutation was associated with longer survival in grade II astrocytoma and GBM (Glioblastoma. Here we analyzed the MGMT promoter methylation status in patients with a known mutation status in codon 132 of IDH1, followed by clinical and genetic data analysis based on the two statuses. After a subtotal tumor resection, the patients were treated using IMRT (Intensity-Modulated Radiation Therapy with 6 MeV photons. The total dose was: 54 Gy for astrocytoma II, 60 Gy for astrocytoma III, 60 Gy for glioblastoma, 2 Gy per day, with 24 h intervals, five days per week. The patients with MGMT promoter methylation and IDH1 somatic mutation (OS = 40 months had a better prognosis than those with MGMT methylation alone (OS = 18 months. In patients with astrocytoma anaplasticum (n = 7 with the IDH1 p.R132H mutation and hypermethylated MGMT, the prognosis was particularly favorable (median OS = 47 months. In patients with astrocytoma II meeting the above criteria, the prognosis was also better than in those not meeting those criteria. The IDH1 mutation appears more relevant for the prognosis than MGMT methylation. The IDH1 p.R132H mutation combined with MGMT hypermethylation seems to be the most advantageous for treatment success. Patients not meeting those criteria may require more aggressive treatments.

  10. Improved Time to Notification of Impending Brain Death and Increased Organ Donation Using an Electronic Clinical Decision Support System.

    Science.gov (United States)

    Zier, J L; Spaulding, A B; Finch, M; Verschaetse, T; Tarrago, R

    2017-08-01

    Early referral of patients to an organ procurement organization (OPO) may positively affect donation outcomes. We implemented an electronic clinic decision support (CDS) system to automatically notify our OPO of children meeting clinical triggers indicating impending brain death. Medical records of all patients who died in a pediatric critical care unit or were referred for imminent death for 3 years prior to installation of the initial CDS (pre-CDS) and for 1 year after implementation of the final CDS (post-CDS) were reviewed. Mean time to OPO notification decreased from 30.2 h pre-CDS to 1.7 h post-CDS (p = 0.015). Notification within 1 h of meeting criteria increased from 36% pre-CDS to 70% post-CDS (p = 0.003). Although an increase in donor conversion from 50% pre-CDS to 90% post-CDS did not reach statistical significance (p = 0.0743), there were more organ donors post-CDS (11 of 24 deaths) than pre-CDS (seven of 57 deaths; p = 0.002). Positive outcomes were achieved with the use of a fully automated CDS system while simultaneously realizing few false-positive notifications, low costs, and minimal workflow interruption. Use of an electronic CDS system in a pediatric hospital setting improved timely OPO notification and was associated with increased organ donation. © 2017 The American Society of Transplantation and the American Society of Transplant Surgeons.

  11. Automated extraction, labelling and analysis of the coronary vasculature from arteriograms

    NARCIS (Netherlands)

    Dumay, A.C.M.; Gerbrands, J.J.; Reiber, J.H.C.

    1996-01-01

    For clinical decision-making and documentation purposes we have developed techniques to extract, label and analyze the coronary vasculature from arteriograms in an automated, quantitative manner. Advanced image processing techniques were applied to extract and analyze the vasculatures from

  12. Computerized clinical decision support systems for chronic disease management: A decision-maker-researcher partnership systematic review

    Directory of Open Access Journals (Sweden)

    Navarro Tamara

    2011-08-01

    Full Text Available Abstract Background The use of computerized clinical decision support systems (CCDSSs may improve chronic disease management, which requires recurrent visits to multiple health professionals, ongoing disease and treatment monitoring, and patient behavior modification. The objective of this review was to determine if CCDSSs improve the processes of chronic care (such as diagnosis, treatment, and monitoring of disease and associated patient outcomes (such as effects on biomarkers and clinical exacerbations. Methods We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for potentially eligible articles published up to January 2010. We included randomized controlled trials that compared the use of CCDSSs to usual practice or non-CCDSS controls. Trials were eligible if at least one component of the CCDSS was designed to support chronic disease management. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of relevant outcomes. Results Of 55 included trials, 87% (n = 48 measured system impact on the process of care and 52% (n = 25 of those demonstrated statistically significant improvements. Sixty-five percent (36/55 of trials measured impact on, typically, non-major (surrogate patient outcomes, and 31% (n = 11 of those demonstrated benefits. Factors of interest to decision makers, such as cost, user satisfaction, system interface and feature sets, unique design and deployment characteristics, and effects on user workflow were rarely investigated or reported. Conclusions A small majority (just over half of CCDSSs improved care processes in chronic disease management and some improved patient health. Policy makers, healthcare administrators, and practitioners should be aware that the evidence of CCDSS effectiveness is limited, especially with respect to the small number and size of studies

  13. Sensitivity of a Clinical Decision Rule and Early Computed Tomography in Aneurysmal Subarachnoid Hemorrhage

    Directory of Open Access Journals (Sweden)

    Dustin G. Mark

    2015-10-01

    Full Text Available Introduction: Application of a clinical decision rule for subarachnoid hemorrhage, in combination with cranial computed tomography (CT performed within six hours of ictus (early cranial CT, may be able to reasonably exclude a diagnosis of aneurysmal subarachnoid hemorrhage (aSAH. This study’s objective was to examine the sensitivity of both early cranial CT and a previously validated clinical decision rule among emergency department (ED patients with aSAH and a normal mental status. Methods: Patients were evaluated in the 21 EDs of an integrated health delivery system between January 2007 and June 2013. We identified by chart review a retrospective cohort of patients diagnosed with aSAH in the setting of a normal mental status and performance of early cranial CT. Variables comprising the SAH clinical decision rule (age >40, presence of neck pain or stiffness, headache onset with exertion, loss of consciousness at headache onset were abstracted from the chart and assessed for inter-rater reliability. Results: One hundred fifty-five patients with aSAH met study inclusion criteria. The sensitivity of early cranial CT was 95.5% (95% CI [90.9-98.2]. The sensitivity of the SAH clinical decision rule was also 95.5% (95% CI [90.9-98.2]. Since all false negative cases for each diagnostic modality were mutually independent, the combined use of both early cranial CT and the clinical decision rule improved sensitivity to 100% (95% CI [97.6-100.0]. Conclusion: Neither early cranial CT nor the SAH clinical decision rule demonstrated ideal sensitivity for aSAH in this retrospective cohort. However, the combination of both strategies might optimize sensitivity for this life-threatening disease.

  14. Clinical decision making in the recognition of dying: a qualitative interview study.

    Science.gov (United States)

    Taylor, Paul; Dowding, Dawn; Johnson, Miriam

    2017-01-25

    Recognising dying is an essential clinical skill for general and palliative care professionals alike. Despite the high importance, both identification and good clinical care of the dying patient remains extremely difficult and often controversial in clinical practice. This study aimed to answer the question: "What factors influence medical and nursing staff when recognising dying in end-stage cancer and heart failure patients?" This study used a descriptive approach to decision-making theory. Participants were purposively sampled for profession (doctor or nurse), specialty (cardiology or oncology) and grade (senior vs junior). Recruitment continued until data saturation was reached. Semi-structured interviews were conducted with NHS medical and nursing staff in an NHS Trust which contained cancer and cardiology tertiary referral centres. An interview schedule was designed, based on decision-making literature. Interviews were audio-recorded and transcribed and analysed using thematic framework. Data were managed with Atlas.ti. Saturation was achieved with 19 participants (7 seniors; 8 intermediate level staff; 4 juniors). There were 11 oncologists (6 doctors, 5 nurses) and 8 cardiologists (3 doctors, 5 nurses). Six themes were generated: information used; decision processes; modifying factors; implementation; reflecting on decisions and related decisions. The decision process described was time-dependent, ongoing and iterative, and relies heavily on intuition. This study supports the need to recognise the strengths and weaknesses of expertise and intuition as part of the decision process, and of placing the recognition of dying in a time-dependent context. Clinicians should also be prepared to accept and convey the uncertainty surrounding these decisions, both in practice and in communication with patients and carers.

  15. Potential Impact on Clinical Decision Making via a Genome-Wide Expression Profiling: A Case Report

    Directory of Open Access Journals (Sweden)

    Hyun Kim

    2016-11-01

    Full Text Available Management of men with prostate cancer is fraught with uncertainty as physicians and patients balance efficacy with potential toxicity and diminished quality of life. Utilization of genomics as a prognostic biomarker has improved the informed decision-making process by enabling more rationale treatment choices. Recently investigations have begun to determine whether genomic information from tumor transcriptome data can be used to impact clinical decision-making beyond prognosis. Here we discuss the potential of genomics to alter management of a patient who presented with high-risk prostate adenocarcinoma. We suggest that this information help selecting patients for advanced imaging, chemotherapies, or clinical trial.

  16. Managed care and clinical decision-making in child and adolescent behavioral health: provider perceptions.

    Science.gov (United States)

    Yanos, Philip T; Garcia, Christine I; Hansell, Stephen; Rosato, Mark G; Minsky, Shula

    2003-03-01

    This study investigated how managed care affects clinical decision-making in a behavioral health care system. Providers serving children and adolescents under both managed and unmanaged care (n = 28) were interviewed about their awareness of differences between the benefit arrangements, how benefits affect clinical decision-making, outcomes and quality of care; and satisfaction with care. Quantitative and qualitative findings indicated that providers saw both advantages and disadvantages to managed care. Although most providers recognized the advantages of managed care in increasing efficiency, many were concerned that administrative pressures associated with managed care compromise service quality.

  17. Many faces of rationality: Implications of the great rationality debate for clinical decision-making.

    Science.gov (United States)

    Djulbegovic, Benjamin; Elqayam, Shira

    2017-10-01

    Given that more than 30% of healthcare costs are wasted on inappropriate care, suboptimal care is increasingly connected to the quality of medical decisions. It has been argued that personal decisions are the leading cause of death, and 80% of healthcare expenditures result from physicians' decisions. Therefore, improving healthcare necessitates improving medical decisions, ie, making decisions (more) rational. Drawing on writings from The Great Rationality Debate from the fields of philosophy, economics, and psychology, we identify core ingredients of rationality commonly encountered across various theoretical models. Rationality is typically classified under umbrella of normative (addressing the question how people "should" or "ought to" make their decisions) and descriptive theories of decision-making (which portray how people actually make their decisions). Normative theories of rational thought of relevance to medicine include epistemic theories that direct practice of evidence-based medicine and expected utility theory, which provides the basis for widely used clinical decision analyses. Descriptive theories of rationality of direct relevance to medical decision-making include bounded rationality, argumentative theory of reasoning, adaptive rationality, dual processing model of rationality, regret-based rationality, pragmatic/substantive rationality, and meta-rationality. For the first time, we provide a review of wide range of theories and models of rationality. We showed that what is "rational" behaviour under one rationality theory may be irrational under the other theory. We also showed that context is of paramount importance to rationality and that no one model of rationality can possibly fit all contexts. We suggest that in context-poor situations, such as policy decision-making, normative theories based on expected utility informed by best research evidence may provide the optimal approach to medical decision-making, whereas in the context

  18. Automated electrocardiogram interpretation programs versus cardiologists' triage decision making based on teletransmitted data in patients with suspected acute coronary syndrome

    DEFF Research Database (Denmark)

    Clark, Elaine N; Ripa, Maria Sejersten; Clemmensen, Peter

    2010-01-01

    The aims of this study were to assess the effectiveness of 2 automated electrocardiogram interpretation programs in patients with suspected acute coronary syndrome transported to hospital by ambulance in 1 rural region of Denmark with hospital discharge diagnosis used as the gold standard...

  19. Preparing Electronic Clinical Data for Quality Improvement and Comparative Effectiveness Research: The SCOAP CERTAIN Automation and Validation Project.

    Science.gov (United States)

    Devine, Emily Beth; Capurro, Daniel; van Eaton, Erik; Alfonso-Cristancho, Rafael; Devlin, Allison; Yanez, N David; Yetisgen-Yildiz, Meliha; Flum, David R; Tarczy-Hornoch, Peter

    2013-01-01

    The field of clinical research informatics includes creation of clinical data repositories (CDRs) used to conduct quality improvement (QI) activities and comparative effectiveness research (CER). Ideally, CDR data are accurately and directly abstracted from disparate electronic health records (EHRs), across diverse health-systems. Investigators from Washington State's Surgical Care Outcomes and Assessment Program (SCOAP) Comparative Effectiveness Research Translation Network (CERTAIN) are creating such a CDR. This manuscript describes the automation and validation methods used to create this digital infrastructure. SCOAP is a QI benchmarking initiative. Data are manually abstracted from EHRs and entered into a data management system. CERTAIN investigators are now deploying Caradigm's Amalga™ tool to facilitate automated abstraction of data from multiple, disparate EHRs. Concordance is calculated to compare data automatically to manually abstracted. Performance measures are calculated between Amalga and each parent EHR. Validation takes place in repeated loops, with improvements made over time. When automated abstraction reaches the current benchmark for abstraction accuracy - 95% - itwill 'go-live' at each site. A technical analysis was completed at 14 sites. Five sites are contributing; the remaining sites prioritized meeting Meaningful Use criteria. Participating sites are contributing 15-18 unique data feeds, totaling 13 surgical registry use cases. Common feeds are registration, laboratory, transcription/dictation, radiology, and medications. Approximately 50% of 1,320 designated data elements are being automatically abstracted-25% from structured data; 25% from text mining. In semi-automating data abstraction and conducting a rigorous validation, CERTAIN investigators will semi-automate data collection to conduct QI and CER, while advancing the Learning Healthcare System.

  20. Automated identification of wound information in clinical notes of patients with heart diseases: Developing and validating a natural language processing application.

    Science.gov (United States)

    Topaz, Maxim; Lai, Kenneth; Dowding, Dawn; Lei, Victor J; Zisberg, Anna; Bowles, Kathryn H; Zhou, Li

    2016-12-01

    Electronic health records are being increasingly used by nurses with up to 80% of the health data recorded as free text. However, only a few studies have developed nursing-relevant tools that help busy clinicians to identify information they need at the point of care. This study developed and validated one of the first automated natural language processing applications to extract wound information (wound type, pressure ulcer stage, wound size, anatomic location, and wound treatment) from free text clinical notes. First, two human annotators manually reviewed a purposeful training sample (n=360) and random test sample (n=1100) of clinical notes (including 50% discharge summaries and 50% outpatient notes), identified wound cases, and created a gold standard dataset. We then trained and tested our natural language processing system (known as MTERMS) to process the wound information. Finally, we assessed our automated approach by comparing system-generated findings against the gold standard. We also compared the prevalence of wound cases identified from free-text data with coded diagnoses in the structured data. The testing dataset included 101 notes (9.2%) with wound information. The overall system performance was good (F-measure is a compiled measure of system's accuracy=92.7%), with best results for wound treatment (F-measure=95.7%) and poorest results for wound size (F-measure=81.9%). Only 46.5% of wound notes had a structured code for a wound diagnosis. The natural language processing system achieved good performance on a subset of randomly selected discharge summaries and outpatient notes. In more than half of the wound notes, there were no coded wound diagnoses, which highlight the significance of using natural language processing to enrich clinical decision making. Our future steps will include expansion of the application's information coverage to other relevant wound factors and validation of the model with external data. Copyright © 2016 Elsevier Ltd. All

  1. Combining data and meta-analysis to build Bayesian networks for clinical decision support.

    Science.gov (United States)

    Yet, Barbaros; Perkins, Zane B; Rasmussen, Todd E; Tai, Nigel R M; Marsh, D William R

    2014-12-01

    Complex clinical decisions require the decision maker to evaluate multiple factors that may interact with each other. Many clinical studies, however, report 'univariate' relations between a single factor and outcome. Such univariate statistics are often insufficient to provide useful support for complex clinical decisions even when they are pooled using meta-analysis. More useful decision support could be provided by evidence-based models that take the interaction between factors into account. In this paper, we propose a method of integrating the univariate results of a meta-analysis with a clinical dataset and expert knowledge to construct multivariate Bayesian network (BN) models. The technique reduces the size of the dataset needed to learn the parameters of a model of a given complexity. Supplementing the data with the meta-analysis results avoids the need to either simplify the model - ignoring some complexities of the problem - or to gather more data. The method is illustrated by a clinical case study into the prediction of the viability of severely injured lower extremities. The case study illustrates the advantages of integrating combined evidence into BN development: the BN developed using our method outperformed four different data-driven structure learning methods, and a well-known scoring model (MESS) in this domain. Copyright © 2014 Elsevier Inc. All rights reserved.

  2. Automated precolumn derivatization procedures in HPLC for biomedical and clinical applications

    NARCIS (Netherlands)

    Wolf, Johannes Hendrik

    1992-01-01

    This thesis describes three automated precolumn derivatization procedures for the analysis of carboxylic group-containing compounds. After derivatization with a suitable label, the derivatives are separated on reversed-phashed HPLC and detected by fluorescence. ... Zie: Summary

  3. A system dynamics model of clinical decision thresholds for the detection of developmental-behavioral disorders

    Directory of Open Access Journals (Sweden)

    R. Christopher Sheldrick

    2016-11-01

    Full Text Available Abstract Background Clinical decision-making has been conceptualized as a sequence of two separate processes: assessment of patients’ functioning and application of a decision threshold to determine whether the evidence is sufficient to justify a given decision. A range of factors, including use of evidence-based screening instruments, has the potential to influence either or both processes. However, implementation studies seldom specify or assess the mechanism by which screening is hypothesized to influence clinical decision-making, thus limiting their ability to address unexpected findings regarding clinicians’ behavior. Building on prior theory and empirical evidence, we created a system dynamics (SD model of how physicians’ clinical decisions are influenced by their assessments of patients and by factors that may influence decision thresholds, such as knowledge of past patient outcomes. Using developmental-behavioral disorders as a case example, we then explore how referral decisions may be influenced by changes in context. Specifically, we compare predictions from the SD model to published implementation trials of evidence-based screening to understand physicians’ management of positive screening results and changes in referral rates. We also conduct virtual experiments regarding the influence of a variety of interventions that may influence physicians’ thresholds, including improved access to co-located mental health care and improved feedback systems regarding patient outcomes. Results Results of the SD model were consistent with recent implementation trials. For example, the SD model suggests that if screening improves physicians’ accuracy of assessment without also influencing decision thresholds, then a significant proportion of children with positive screens will not be referred and the effect of screening implementation on referral rates will be modest—results that are consistent with a large proportion of published

  4. Building a normative decision support system for clinical and operational risk management in hemodialysis.

    Science.gov (United States)

    Cornalba, Chiara; Bellazzi, Roberto G; Bellazzi, Riccardo

    2008-09-01

    This paper describes the design and implementation of a decision support system for risk management in hemodialysis (HD) departments. The proposed system exploits a domain ontology to formalize the problem as a Bayesian network. It also relies on a software tool, able to automatically collect HD data, to learn the network conditional probabilities. By merging prior knowledge and the available data, the system allows to estimate risk profiles both for patients and HD departments. The risk management process is completed by an influence diagram that enables scenario analysis to choose the optimal decisions that mitigate a patient's risk. The methods and design of the decision support tool are described in detail, and the derived decision model is presented. Examples and case studies are also shown. The tool is one of the few examples of normative system explicitly conceived to manage operational and clinical risks in health care environments.

  5. The role of emotion in clinical decision making: an integrative literature review.

    Science.gov (United States)

    Kozlowski, Desirée; Hutchinson, Marie; Hurley, John; Rowley, Joanne; Sutherland, Joanna

    2017-12-15

    Traditionally, clinical decision making has been perceived as a purely rational and cognitive process. Recently, a number of authors have linked emotional intelligence (EI) to clinical decision making (CDM) and calls have been made for an increased focus on EI skills for clinicians. The objective of this integrative literature review was to identify and synthesise the empirical evidence for a role of emotion in CDM. A systematic search of the bibliographic databases PubMed, PsychINFO, and CINAHL (EBSCO) was conducted to identify empirical studies of clinician populations. Search terms were focused to identify studies reporting clinician emotion OR clinician emotional intelligence OR emotional competence AND clinical decision making OR clinical reasoning. Twenty three papers were retained for synthesis. These represented empirical work from qualitative, quantitative, and mixed-methods approaches and comprised work with a focus on experienced emotion and on skills associated with emotional intelligence. The studies examined nurses (10), physicians (7), occupational therapists (1), physiotherapists (1), mixed clinician samples (3), and unspecified infectious disease experts (1). We identified two main themes in the context of clinical decision making: the subjective experience of emotion; and, the application of emotion and cognition in CDM. Sub-themes under the subjective experience of emotion were: emotional response to contextual pressures; emotional responses to others; and, intentional exclusion of emotion from CDM. Under the application of emotion and cognition in CDM, sub-themes were: compassionate emotional labour - responsiveness to patient emotion within CDM; interdisciplinary tension regarding the significance and meaning of emotion in CDM; and, emotion and moral judgement. Clinicians' experienced emotions can and do affect clinical decision making, although acknowledgement of that is far from universal. Importantly, this occurs in the in the absence of a

  6. Supporting therapy selection in computerized clinical guidelines by means of decision theory.

    Science.gov (United States)

    Montani, Stefania; Terenziani, Paolo; Bottrighi, Alessio

    2007-01-01

    Supporting therapy selection is a fundamental task for a system for the computerized management of clinical guidelines (GL). The goal is particularly critical when no alternative is really better than the others, from a strictly clinical viewpoint. In these cases, decision theory appears to be a very suitable means to provide advice. In this paper, we describe how algorithms for calculating utility, and for evaluating the optimal policy, can be exploited to fit the GL management context.

  7. Paying for treatments? Influences on negotiating clinical need and decision-making for dental implant treatment

    OpenAIRE

    Exley, Catherine E; Rousseau, Nikki S; Steele, Jimmy; Finch, Tracy; Field, James; Donaldson, Cam; Thomason, J Mark; May, Carl R; Ellis, Janice S

    2009-01-01

    Abstract Background The aim of this study is to examine how clinicians and patients negotiate clinical need and treatment decisions within a context of finite resources. Dental implant treatment is an effective treatment for missing teeth, but is only available via the NHS in some specific clinical circumstances. The majority of people who receive this treatment therefore pay privately, often at substantial cost to themselves. People are used to paying towards dental treatment costs. However,...

  8. Competencies in nursing students for organized forms of clinical moral deliberation and decision-making

    NARCIS (Netherlands)

    dr. Bart Cusveller; Jeanette den Uil-Westerlaken

    2014-01-01

    Bachelor-prepared nurses are expected to be competent in moral deliberation and decision-making (MDD) in clinical practice. It is unclear, however, how this competence develops in nursing students. This study explores the development of nursing students’ competence for participating in organized

  9. Knowledge of risk factors and the periodontal disease-systemic link in dental students' clinical decisions.

    Science.gov (United States)

    Friesen, Lynn Roosa; Walker, Mary P; Kisling, Rebecca E; Liu, Ying; Williams, Karen B

    2014-09-01

    This study evaluated second-, third-, and fourth-year dental students' ability to identify systemic conditions associated with periodontal disease, risk factors most important for referral, and medications with an effect on the periodontium and their ability to apply this knowledge to make clinical decisions regarding treatment and referral of periodontal patients. A twenty-one question survey was administered at one U.S. dental school in the spring semester of 2012 to elicit the students' knowledge and confidence regarding clinical reasoning. The response rate was 86 percent. Periodontal risk factors were accurately selected by at least 50 percent of students in all three classes; these were poorly controlled diabetes, ≥6 mm pockets posteriorly, and lack of response to previous non-surgical therapy. Confidence in knowledge, knowledge of risk factors, and knowledge of medications with an effect on the periodontium improved with training and were predictive of better referral decision making. The greatest impact of training was seen on the students' ability to make correct decisions about referral and treatment for seven clinical scenarios. Although the study found a large increase in the students' abilities from the second through fourth years, the mean of 4.6 (out of 7) for the fourth-year students shows that, on average, those students missed correct treatment or referral on more than two of seven clinical cases. These results suggest that dental curricula should emphasize more critical decision making with respect to referral and treatment criteria in managing the periodontal patient.

  10. Doing the right things and doing things right : inpatient drug surveillance assisted by clinical decision support

    NARCIS (Netherlands)

    Helmons, Pieter J.; Suijkerbuijk, Bas O.; Nannan Panday, Prashant V.; Kosterink, Jos G. W.

    Increased budget constraints and a continuous focus on improved quality require an efficient inpatient drug surveillance process. We describe a hospital-wide drug surveillance strategy consisting of a multidisciplinary evaluation of drug surveillance activities and using clinical decision support to

  11. Decision-tree induction to detect clinical mastitis with automatic milking

    NARCIS (Netherlands)

    Kamphuis, C.; Mollenhorst, H.; Feelders, A.; Pietersma, D.; Hogeveen, H.

    2010-01-01

    a b s t r a c t This study explored the potential of using decision-tree induction to develop models for the detection of clinical mastitis with automatic milking. Sensor data (including electrical conductivity and colour) of over 711,000 quarter milkings were collected from December 2006 till

  12. Clinical decision-making to facilitate appropriate patient management in chiropractic practice: 'the 3-questions model'

    Directory of Open Access Journals (Sweden)

    Amorin-Woods Lyndon G

    2012-03-01

    Full Text Available Abstract Background A definitive diagnosis in chiropractic clinical practice is frequently elusive, yet decisions around management are still necessary. Often, a clinical impression is made after the exclusion of serious illness or injury, and care provided within the context of diagnostic uncertainty. Rather than focussing on labelling the condition, the clinician may choose to develop a defendable management plan since the response to treatment often clarifies the diagnosis. Discussion This paper explores the concept and elements of defensive problem-solving practice, with a view to developing a model of agile, pragmatic decision-making amenable to real-world application. A theoretical framework that reflects the elements of this approach will be offered in order to validate the potential of a so called '3-Questions Model'; Summary Clinical decision-making is considered to be a key characteristic of any modern healthcare practitioner. It is, thus, prudent for chiropractors to re-visit the concept of defensible practice with a view to facilitate capable clinical decision-making and competent patient examination skills. In turn, the perception of competence and trustworthiness of chiropractors within the wider healthcare community helps integration of chiropractic services into broader healthcare settings.

  13. Improving Emergency Department Triage Classification with Computerized Clinical Decision Support at a Pediatric Hospital

    Science.gov (United States)

    Kunisch, Joseph Martin

    2012-01-01

    Background: The Emergency Severity Index (ESI) is an emergency department (ED) triage classification system based on estimated patient-specific resource utilization. Rules for a computerized clinical decision support (CDS) system based on a patient's chief complaint were developed and tested using a stochastic model for predicting ESI scores.…

  14. Teaching metacognition in clinical decision-making using a novel mnemonic checklist: an exploratory study.

    Science.gov (United States)

    Chew, Keng Sheng; Durning, Steven J; van Merriënboer, Jeroen Jg

    2016-12-01

    Metacognition is a cognitive debiasing strategy that clinicians can use to deliberately detach themselves from the immediate context of a clinical decision, which allows them to reflect upon the thinking process. However, cognitive debiasing strategies are often most needed when the clinician cannot afford the time to use them. A mnemonic checklist known as TWED (T = threat, W = what else, E = evidence and D = dispositional factors) was recently created to facilitate metacognition. This study explores the hypothesis that the TWED checklist improves the ability of medical students to make better clinical decisions. Two groups of final-year medical students from Universiti Sains Malaysia, Malaysia, were recruited to participate in this quasi-experimental study. The intervention group (n = 21) received educational intervention that introduced the TWED checklist, while the control group (n = 19) received a tutorial on basic electrocardiography. Post-intervention, both groups received a similar assessment on clinical decision-making based on five case scenarios. The mean score of the intervention group was significantly higher than that of the control group (18.50 ± 4.45 marks vs. 12.50 ± 2.84 marks, p metacognition in clinical decision-making. Copyright: © Singapore Medical Association

  15. Forms of Knowledge Incorporated in Clinical Decision-making among Newly-Graduated Nurses: A Metasynthesis

    DEFF Research Database (Denmark)

    Voldbjerg, Siri; Elgaard Sørensen, Erik; Grønkjær, Mette

    2014-01-01

    the knowledge that informs clinical decision-making among newly-graduated nurses. Qualitative studies were retrieved from CINAHL, PubMed, SCOPE, ERIC and GOOGLE-Scholar and subsequently selected by pre-defined inclusion criteria and critically appraised using CASP. Metaphors identified in the analytical process...

  16. Automated, simple, and efficient influenza RNA extraction from clinical respiratory swabs using TruTip and epMotion.

    Science.gov (United States)

    Griesemer, Sara B; Holmberg, Rebecca; Cooney, Christopher G; Thakore, Nitu; Gindlesperger, Alissa; Knickerbocker, Christopher; Chandler, Darrell P; St George, Kirsten

    2013-09-01

    Rapid, simple and efficient influenza RNA purification from clinical samples is essential for sensitive molecular detection of influenza infection. Automation of the TruTip extraction method can increase sample throughput while maintaining performance. To automate TruTip influenza RNA extraction using an Eppendorf epMotion robotic liquid handler, and to compare its performance to the bioMerieux easyMAG and Qiagen QIAcube instruments. Extraction efficacy and reproducibility of the automated TruTip/epMotion protocol was assessed from influenza-negative respiratory samples spiked with influenza A and B viruses. Clinical extraction performance from 170 influenza A and B-positive respiratory swabs was also evaluated and compared using influenza A and B real-time RT-PCR assays. TruTip/epMotion extraction efficacy was 100% in influenza virus-spiked samples with at least 745 influenza A and 370 influenza B input gene copies per extraction, and exhibited high reproducibility over four log10 concentrations of virus (extraction were also positive following TruTip extraction. Overall Ct value differences obtained between TruTip/epMotion and easyMAG/QIAcube clinical extracts ranged from 1.24 to 1.91. Pairwise comparisons of Ct values showed a high correlation of the TruTip/epMotion protocol to the other methods (R2>0.90). The automated TruTip/epMotion protocol is a simple and rapid extraction method that reproducibly purifies influenza RNA from respiratory swabs, with comparable efficacy and efficiency to both the easyMAG and QIAcube instruments. Copyright © 2013 Elsevier B.V. All rights reserved.

  17. Advances in Clinical Decision Support: Highlights of Practice and the Literature 2015-2016.

    Science.gov (United States)

    Jenders, R A

    2017-08-01

    Introduction: Advances in clinical decision support (CDS) continue to evolve to support the goals of clinicians, policymakers, patients and professional organizations to improve clinical practice, patient safety, and the quality of care. Objectives: Identify key thematic areas or foci in research and practice involving clinical decision support during the 2015-2016 time period. Methods: Thematic analysis consistent with a grounded theory approach was applied in a targeted review of journal publications, the proceedings of key scientific conferences as well as activities in standards development organizations in order to identify the key themes underlying work related to CDS. Results: Ten key thematic areas were identified, including: 1) an emphasis on knowledge representation, with a focus on clinical practice guidelines; 2) various aspects of precision medicine, including the use of sensor and genomic data as well as big data; 3) efforts in quality improvement; 4) innovative uses of computer-based provider order entry (CPOE) systems, including relevant data displays; 5) expansion of CDS in various clinical settings; 6) patient-directed CDS; 7) understanding the potential negative impact of CDS; 8) obtaining structured data to drive CDS interventions; 9) the use of diagnostic decision support; and 10) the development and use of standards for CDS. Conclusions: Active research and practice in 2015-2016 continue to underscore the importance and broad utility of CDS for effecting change and improving the quality and outcome of clinical care. Georg Thieme Verlag KG Stuttgart.

  18. Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials.

    Science.gov (United States)

    Jonnalagadda, Siddhartha R; Adupa, Abhishek K; Garg, Ravi P; Corona-Cox, Jessica; Shah, Sanjiv J

    2017-06-01

    Precision medicine requires clinical trials that are able to efficiently enroll subtypes of patients in whom targeted therapies can be tested. To reduce the large amount of time spent screening, identifying, and recruiting patients with specific subtypes of heterogeneous clinical syndromes (such as heart failure with preserved ejection fraction [HFpEF]), we need prescreening systems that are able to automate data extraction and decision-making tasks. However, a major obstacle is the vast amount of unstructured free-form text in medical records. Here we describe an information extraction-based approach that automatically converts unstructured text into structured data, which is cross-referenced against eligibility criteria using a rule-based system to determine which patients qualify for a major HFpEF clinical trial (PARAGON). We show that we can achieve a sensitivity and positive predictive value of 0.95 and 0.86, respectively. Our open-source algorithm could be used to efficiently identify and subphenotype patients with HFpEF and other disorders.

  19. Clinic-based Point of Care Transabdominal Ultrasound for Monitoring Crohn's Disease: Impact on Clinical Decision Making.

    Science.gov (United States)

    Novak, Kerri; Tanyingoh, Divine; Petersen, Frauke; Kucharzik, Torsten; Panaccione, Remo; Ghosh, Subrata; Kaplan, Gilaad G; Wilson, Alex; Kannengiesser, Klaus; Maaser, Christian

    2015-09-01

    The use of cross-sectional imaging is important to characterise inflammatory bowel disease [IBD] activity, extent, and location and to exclude complications, regardless of symptoms. The aim of this study was to evaluate the impact of routine use of sonography in the management of inflammatory bowel disease. A total of 49 patients with Crohn's disease were prospectively evaluated. Clinical symptoms (Harvey-Bradshaw Index [HBI]), disease character, serological markers of inflammation [C-reactive protein], and endoscopic evaluation were collected and reviewed by two independent IBD-specialty physicians. Clinical decisions regarding management were recorded. A separate, blinded physician then performed bowel ultrasound [US] and graded disease activity:] as inactive, mild, or active. A second blinded physician read and graded a sub-set of the US images. Clinical decisions of both IBD-physicians after US were independently recorded. Changes in clinical management following US information and inter-rater agreement on US disease activity parameters were evaluated. The concordance between US, CRP and clinical symptoms [HBI] were analysed. Follow-up data after US evaluation were collected. Clinical decisions were changed after ultrasound assessment in 30/49 [60%] and 28/48 [58%] of cases, for each physician respectively [p management. The agreement in overall score between the US reviewers was good, ĸ = 0.749 [0.5814, 0.9180], p management and is an important adjunct to routine clinical and laboratory assessment. Copyright © 2015 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  20. Application of the Stockholm Hierarchy to Defining the Quality of Reference Intervals and Clinical Decision Limits

    Science.gov (United States)

    Sikaris, Ken

    2012-01-01

    The Stockholm Hierarchy is a professional consensus created to define the preferred approaches to defining analytical quality. The quality of a laboratory measurement can also be classified by the quality of the limits that the value is compared with, namely reference interval limits and clinical decision limits. At the highest level in the hierarchy would be placed clinical decision limits based on clinical outcome studies. The second level would include both formal reference interval studies (studies of intra and inter-individual variations) and clinical decision limits based on clinician survey. While these approaches are commonly used, they require a lot of resources to define accurately. Placing laboratory experts on the third level would suggest that although they can also define reference intervals by consensus, theirs aren’t as well regarded as clinician defined limits which drive clinical behaviour. Ideally both analytical and clinical considerations should be made, with clinicians and laboratorians both having important information to consider. The fourth level of reference intervals would be for those defined by survey or by regulatory authorities because of the focus on what is commonly achieved rather than what is necessarily correct. Finally, laboratorians know that adopting reference limits from kit inserts or textbook publications is problematic because both methodological issues and reference populations are often not the same as their own. This approach would rank fifth and last. When considering which so called ‘common’ or ‘harmonised reference intervals’ to adopt, both these characteristics and the quality of individual studies need to be assessed. Finally, we should also be aware that reference intervals describe health and physiology while clinical decision limits focus on disease and pathology, and unless we understand and consider the two corresponding issues of test specificity and test sensitivity, we cannot assure the quality of

  1. Automated grading for diabetic retinopathy: a large-scale audit using arbitration by clinical experts.

    Science.gov (United States)

    Fleming, Alan D; Goatman, Keith A; Philip, Sam; Prescott, Gordon J; Sharp, Peter F; Olson, John A

    2010-12-01

    Automated grading software has the potential to reduce the manual grading workload within diabetic retinopathy screening programmes. This audit was undertaken at the request of Scotland's National Diabetic Retinopathy Screening Collaborative to assess whether the introduction of automated grading software into the national screening programme would be safe, robust and effective. Automated grading, performed by software for image quality assessment and for microaneurysm/dot haemorrhage detection, was carried out on 78,601 images, obtained from 33,535 consecutive patients, which had been manually graded at one of two regional diabetic retinopathy screening programmes. Cases where the automated grading software assessment indicated gradable images with no disease but the screening programme indicated ungradable images or disease more severe than mild retinopathy were arbitrated by seven senior ophthalmologists. 100% (180/180) of patients with proliferative retinopathy, 100% (324/324) with referable background retinopathy, 100% (193/193) with observable background retinopathy, 97.3% (1099/1130) with referable maculopathy, 99.2% (384/387) with observable maculopathy and 99.8% (1824/1827) with ungradable images were detected by the software. The automated grading software operated to previously published results when applied to a large, unselected population attending two regional screening programmes. Manual grading workload reduction would be 36.3%.

  2. Attitudes and decision making among women seeking abortions at one U.S. clinic.

    Science.gov (United States)

    Foster, Diana Greene; Gould, Heather; Taylor, Jessica; Weitz, Tracy A

    2012-06-01

    Various restrictions on abortion have been imposed under the pretense that women may be uninformed, undecided or coerced in regard to their decision to terminate a pregnancy. Understanding whether certain women are at risk of low confidence in their abortion decision is useful for providing client-centered care and allocating counseling time to women with the greatest needs. Data were abstracted from the precounseling needs assessment form and clinical intake form of 5,109 women who sought 5,387 abortions at one U.S. clinic in 2008. Multivariate logistic regression was used to analyze variables associated with women's high confidence in their abortion decision. For 87% of the abortions sought, women had high confidence in their decision before receiving counseling. Certain variables were negatively associated with abortions' being sought by women with high confidence: being younger than 20, being black, not having a high school diploma, having a history of depression, having a fetus with an anomaly, having general difficulty making decisions, having spiritual concerns, believing that abortion is killing and fearing not being forgiven by God (odds ratios, 0.2-0.8). Having a supportive mother or male partner was associated with increased odds of high confidence (1.3 and 1.2, respectively). Regulations requiring state-approved information or waiting periods may not meet the complex needs of all women. Instead, women may benefit more from interactions with trained staff who can assess and respond to their individual needs. Copyright © 2012 by the Guttmacher Institute.

  3. Applicability Of A Semi-Automated Clinical Chemistry Analyzer In Determining The Antioxidant Concentrations Of Selected Plants

    Directory of Open Access Journals (Sweden)

    Allan L. Hilario

    2017-07-01

    Full Text Available Plants are rich sources of antioxidants that are protective against diseases associated to oxidative stress. There is a need for high throughput screening method that should be useful in determining the antioxidant concentration in plants. Such screening method should significantly simplify and speed up most antioxidant assays. This paper aimed at comparing the applicability of a semi-automated clinical chemistry analyzer Pointe Scientific MI USA with the traditional standard curve method and using a Vis spectrophotometer in performing the DPPH assay for antioxidant screening. Samples of crude aqueous leaf extract of kulitis Amaranthus viridis Linn and chayote Sechium edule Linn were screened for the Total Antioxidant Concentration TAC using the two methods. Results presented in mean SD amp956gdl were compared using unpaired Students t-test P0.05. All runs were done in triplicates. The mean TAC of A. viridis was 646.0 45.5 amp956gdl using the clinical chemistry analyzer and 581.9 19.4 amp956gdl using the standard curve-spectrophotometer. On the other hand the mean TAC of S. edule was 660.2 35.9 amp956gdl using the semi-automated clinical chemistry analyzer and 672.3 20.9 amp956gdl using the spectrophotometer. No significant differences were observed between the readings of the two methods for A. viridis P0.05 and S. edible P0.05. This implies that the clinical chemistry analyzer can be an alternative method in conducting the DPPH assay to determine the TAC in plants. This study presented the applicability of a semi-automated clinical chemistry analyzer in performing the DPPH assay. Further validation can be conducted by performing other antioxidant assays using this equipment.

  4. Development of Decision-Making Automated System for Optimal Placement of Physical Access Control System’s Elements

    Science.gov (United States)

    Danilova, Olga; Semenova, Zinaida

    2018-04-01

    The objective of this study is a detailed analysis of physical protection systems development for information resources. The optimization theory and decision-making mathematical apparatus is used to formulate correctly and create an algorithm of selection procedure for security systems optimal configuration considering the location of the secured object’s access point and zones. The result of this study is a software implementation scheme of decision-making system for optimal placement of the physical access control system’s elements.

  5. Introducing pharmacogenetic testing with clinical decision support into primary care: a feasibility study.

    Science.gov (United States)

    Dawes, Martin; Aloise, Martin N; Ang, J Sidney; Cullis, Pieter; Dawes, Diana; Fraser, Robert; Liknaitzky, Gideon; Paterson, Andrea; Stanley, Paul; Suarez-Gonzalez, Adriana; Katzov-Eckert, Hagit

    2016-01-01

    Inappropriate prescribing increases patient illness and death owing to adverse drug events. The inclusion of genetic information into primary care medication practices is one solution. Our aim was to assess the ability to obtain and genotype saliva samples and to determine the levels of use of a decision support tool that creates medication options adjusted for patient characteristics, drug-drug interactions and pharmacogenetics. We conducted a cohort study in 6 primary care settings (5 family practices and 1 pharmacy), enrolling 191 adults with at least 1 of 10 common diseases. Saliva samples were obtained in the physician's office or pharmacy and sent to our laboratory, where DNA was extracted and genotyped and reports were generated. The reports were sent directly to the family physician/pharmacist and linked to an evidence-based prescribing decision support system. The primary outcome was ability to obtain and genotype samples. The secondary outcomes were yield and purity of DNA samples, ability to link results to decision support software and use of the decision support software. Genotyping resulted in linking of 189 patients (99%) with pharmacogenetic reports to the decision support program. A total of 96.8% of samples had at least 1 actionable genotype for medications included in the decision support system. The medication support system was used by the physicians and pharmacists 236 times over 3 months. Physicians and pharmacists can collect saliva samples of sufficient quantity and quality for DNA extraction, purification and genotyping. A clinical decision support system with integrated data from pharmacogenetic tests may enable personalized prescribing within primary care. Trial registration: ClinicalTrials.gov, NCT02383290.

  6. Assessing an Adolescent's Capacity for Autonomous Decision-Making in Clinical Care.

    Science.gov (United States)

    Michaud, Pierre-André; Blum, Robert Wm; Benaroyo, Lazare; Zermatten, Jean; Baltag, Valentina

    2015-10-01

    The purpose of this article is to provide policy guidance on how to assess the capacity of minor adolescents for autonomous decision-making without a third party authorization, in the field of clinical care. In June 2014, a two-day meeting gathered 20 professionals from all continents, working in the field of adolescent medicine, neurosciences, developmental and clinical psychology, sociology, ethics, and law. Formal presentations and discussions were based on a literature search and the participants' experience. The assessment of adolescent decision-making capacity includes the following: (1) a review of the legal context consistent with the principles of the Convention on the Rights of the Child; (2) an empathetic relationship between the adolescent and the health care professional/team; (3) the respect of the adolescent's developmental stage and capacities; (4) the inclusion, if relevant, of relatives, peers, teachers, or social and mental health providers with the adolescent's consent; (5) the control of coercion and other social forces that influence decision-making; and (6) a deliberative stepwise appraisal of the adolescent's decision-making process. This stepwise approach, already used among adults with psychiatric disorders, includes understanding the different facets of the given situation, reasoning on the involved issues, appreciating the outcomes linked with the decision(s), and expressing a choice. Contextual and psychosocial factors play pivotal roles in the assessment of adolescents' decision-making capacity. The evaluation must be guided by a well-established procedure, and health professionals should be trained accordingly. These proposals are the first to have been developed by a multicultural, multidisciplinary expert panel. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

  7. Optimizing Clinical Decision Support in the Electronic Health Record. Clinical Characteristics Associated with the Use of a Decision Tool for Disposition of ED Patients with Pulmonary Embolism.

    Science.gov (United States)

    Ballard, Dustin W; Vemula, Ridhima; Chettipally, Uli K; Kene, Mamata V; Mark, Dustin G; Elms, Andrew K; Lin, James S; Reed, Mary E; Huang, Jie; Rauchwerger, Adina S; Vinson, David R

    2016-09-21

    Adoption of clinical decision support (CDS) tools by clinicians is often limited by workflow barriers. We sought to assess characteristics associated with clinician use of an electronic health record-embedded clinical decision support system (CDSS). In a prospective study on emergency department (ED) activation of a CDSS tool across 14 hospitals between 9/1/14 to 4/30/15, the CDSS was deployed at 10 active sites with an on-site champion, education sessions, iterative feedback, and up to 3 gift cards/clinician as an incentive. The tool was also deployed at 4 passive sites that received only an introductory educational session. Activation of the CDSS - which calculated the Pulmonary Embolism Severity Index (PESI) score and provided guidance - and associated clinical data were collected prospectively. We used multivariable logistic regression with random effects at provider/facility levels to assess the association between activation of the CDSS tool and characteristics at: 1) patient level (PESI score), 2) provider level (demographics and clinical load at time of activation opportunity), and 3) facility level (active vs. passive site, facility ED volume, and ED acuity at time of activation opportunity). Out of 662 eligible patient encounters, the CDSS was activated in 55%: active sites: 68% (346/512); passive sites 13% (20/150). In bivariate analysis, active sites had an increase in activation rates based on the number of prior gift cards the physician had received (96% if 3 prior cards versus 60% if 0, pactivation (p=0.03). In multivariable analysis, active site status, low ED volume at the time of diagnosis and PESI scores I or II (compared to III or higher) were associated with higher likelihood of CDSS activation. Performing on-site tool promotion significantly increased odds of CDSS activation. Optimizing CDSS adoption requires active education.

  8. Newly graduated nurses' use of knowledge sources in clinical decision-making

    DEFF Research Database (Denmark)

    Voldbjerg, Siri Lygum; Grønkjaer, Mette; Wiechula, Rick

    2017-01-01

    AIMS AND OBJECTIVES: To explore which knowledge sources newly graduated nurses' use in clinical decision-making and why and how they are used. BACKGROUND: In spite of an increased educational focus on skills and competencies within evidence based practice newly graduated nurses' ability to use...... could be used. CONCLUSION AND RELEVANCE TO CLINICAL PRACTICE: Although there is a complexity and variety to knowledge sources used there is an imbalance with the experienced nurse playing a key role, functioning both as predominant source and a role-model as to which sources are valued and used...... approaches to strengthen the knowledgebase used in clinical decision-making. DESIGN AND METHODS: Ethnographic study using participant-observation and individual semi-structured interviews of nine Danish newly graduated nurses in medical and surgical hospital settings. RESULTS: Newly graduates use...

  9. Workshop on using natural language processing applications for enhancing clinical decision making: an executive summary.

    Science.gov (United States)

    Pai, Vinay M; Rodgers, Mary; Conroy, Richard; Luo, James; Zhou, Ruixia; Seto, Belinda

    2014-02-01

    In April 2012, the National Institutes of Health organized a two-day workshop entitled 'Natural Language Processing: State of the Art, Future Directions and Applications for Enhancing Clinical Decision-Making' (NLP-CDS). This report is a summary of the discussions during the second day of the workshop. Collectively, the workshop presenters and participants emphasized the need for unstructured clinical notes to be included in the decision making workflow and the need for individualized longitudinal data tracking. The workshop also discussed the need to: (1) combine evidence-based literature and patient records with machine-learning and prediction models; (2) provide trusted and reproducible clinical advice; (3) prioritize evidence and test results; and (4) engage healthcare professionals, caregivers, and patients. The overall consensus of the NLP-CDS workshop was that there are promising opportunities for NLP and CDS to deliver cognitive support for healthcare professionals, caregivers, and patients.

  10. [A computerised clinical decision-support system for the management of depression in Primary Care].

    Science.gov (United States)

    Aragonès, Enric; Comín, Eva; Cavero, Myriam; Pérez, Víctor; Molina, Cristina; Palao, Diego

    Despite its clinical relevance and its importance as a public health problem, there are major gaps in the management of depression. Evidence-based clinical guidelines are useful to improve processes and clinical outcomes. In order to make their implementation easier these guidelines have been transformed into computerised clinical decision support systems. In this article, a description is presented on the basics and characteristics of a new computerised clinical guideline for the management of major depression, developed in the public health system in Catalonia. This tool helps the clinician to establish reliable and accurate diagnoses of depression, to choose the best treatment a priori according to the disease and the patient characteristics. It also emphasises the importance of systematic monitoring to assess the clinical course, and to adjust therapeutic interventions to the patient's needs at all times. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

  11. Consensus Recommendations for Systematic Evaluation of Drug-Drug Interaction Evidence for Clinical Decision Support

    Science.gov (United States)

    Scheife, Richard T.; Hines, Lisa E.; Boyce, Richard D.; Chung, Sophie P.; Momper, Jeremiah; Sommer, Christine D.; Abernethy, Darrell R.; Horn, John; Sklar, Stephen J.; Wong, Samantha K.; Jones, Gretchen; Brown, Mary; Grizzle, Amy J.; Comes, Susan; Wilkins, Tricia Lee; Borst, Clarissa; Wittie, Michael A.; Rich, Alissa; Malone, Daniel C.

    2015-01-01

    Background Healthcare organizations, compendia, and drug knowledgebase vendors use varying methods to evaluate and synthesize evidence on drug-drug interactions (DDIs). This situation has a negative effect on electronic prescribing and medication information systems that warn clinicians of potentially harmful medication combinations. Objective To provide recommendations for systematic evaluation of evidence from the scientific literature, drug product labeling, and regulatory documents with respect to DDIs for clinical decision support. Methods A conference series was conducted to develop a structured process to improve the quality of DDI alerting systems. Three expert workgroups were assembled to address the goals of the conference. The Evidence Workgroup consisted of 15 individuals with expertise in pharmacology, drug information, biomedical informatics, and clinical decision support. Workgroup members met via webinar from January 2013 to February 2014. Two in-person meetings were conducted in May and September 2013 to reach consensus on recommendations. Results We developed expert-consensus answers to three key questions: 1) What is the best approach to evaluate DDI evidence?; 2) What evidence is required for a DDI to be applicable to an entire class of drugs?; and 3) How should a structured evaluation process be vetted and validated? Conclusion Evidence-based decision support for DDIs requires consistent application of transparent and systematic methods to evaluate the evidence. Drug information systems that implement these recommendations should be able to provide higher quality information about DDIs in drug compendia and clinical decision support tools. PMID:25556085

  12. Physicians' perspectives on communication and decision making in clinical encounters for treatment of latent tuberculosis infection.

    Science.gov (United States)

    Dobler, Claudia C; Bosnic-Anticevich, Sinthia; Armour, Carol L

    2018-01-01

    The aim of the study was to explore the views of tuberculosis (TB) physicians on treatment of latent TB infection (LTBI), focusing on decision making and communication in clinical practice. 20 Australian TB physicians participated in a semistructured interview in person or over the telephone. Interviews were recorded, transcribed and analysed thematically. The study identified challenges that physicians face when discussing treatment for LTBI with patients. These included difficulties explaining the concept of latency (in particular to patients from culturally and linguistically diverse backgrounds) and providing guidance to patients while still framing treatment decisions as a choice. Tailored estimates of the risk of developing TB and the risk of developing an adverse effect from LTBI treatment were considered the most important information for decision making and discussion with patients. Physicians acknowledged that there is a significant amount of unwarranted treatment variation, which they attributed to the lack of evidence about the risk-benefit balance of LTBI treatment in certain scenarios and guidelines that refer to the need for case-by-case decision making in many instances. In order to successfully implement LTBI treatment at a clinical level, consideration should be given to research on how to best address communication challenges arising in clinical encounters.

  13. A Hierarchical Framework for Evaluation and Informed Decision Making Regarding Smartphone Apps for Clinical Care.

    Science.gov (United States)

    Torous, John Blake; Chan, Steven Richard; Gipson, Shih Yee-Marie Tan; Kim, Jung Won; Nguyen, Thuc-Quyen; Luo, John; Wang, Philip

    2018-02-15

    With thousands of smartphone apps targeting mental health, it is difficult to ignore the rapidly expanding use of apps in the treatment of psychiatric disorders. Patients with psychiatric conditions are interested in mental health apps and have begun to use them. That does not mean that clinicians must support, endorse, or even adopt the use of apps, but they should be prepared to answer patients' questions about apps and facilitate shared decision making around app use. This column describes an evaluation framework designed by the American Psychiatric Association to guide informed decision making around the use of smartphone apps in clinical care.

  14. Standardized Ki67 Diagnostics Using Automated Scoring--Clinical Validation in the GeparTrio Breast Cancer Study.

    Science.gov (United States)

    Klauschen, Frederick; Wienert, Stephan; Schmitt, Wolfgang D; Loibl, Sibylle; Gerber, Bernd; Blohmer, Jens-Uwe; Huober, Jens; Rüdiger, Thomas; Erbstößer, Erhard; Mehta, Keyur; Lederer, Bianca; Dietel, Manfred; Denkert, Carsten; von Minckwitz, Gunter

    2015-08-15

    Scoring proliferation through Ki67 immunohistochemistry is an important component in predicting therapy response to chemotherapy in patients with breast cancer. However, recent studies have cast doubt on the reliability of "visual" Ki67 scoring in the multicenter setting, particularly in the lower, yet clinically important, proliferation range. Therefore, an accurate and standardized Ki67 scoring is pivotal both in routine diagnostics and larger multicenter studies. We validated a novel fully automated Ki67 scoring approach that relies on only minimal a priori knowledge on cell properties and requires no training data for calibration. We applied our approach to 1,082 breast cancer samples from the neoadjuvant GeparTrio trial and compared the performance of automated and manual Ki67 scoring. The three groups of autoKi67 as defined by low (≤ 15%), medium (15.1%-35%), and high (>35%) automated scores showed pCR rates of 5.8%, 16.9%, and 29.5%, respectively. AutoKi67 was significantly linked to prognosis with overall and progression-free survival P values P(OS) Ki67 scoring. Moreover, automated Ki67 scoring was an independent prognosticator in the multivariate analysis with P(OS) = 0.002, P(PFS) = 0.009 (autoKi67) versus P(OS) = 0.007, PPFS = 0.004 (manual Ki67). The computer-assisted Ki67 scoring approach presented here offers a standardized means of tumor cell proliferation assessment in breast cancer that correlated with clinical endpoints and is deployable in routine diagnostics. It may thus help to solve recently reported reliability concerns in Ki67 diagnostics. ©2014 American Association for Cancer Research.

  15. COMPARISON BETWEEN AUTOMATED SYSTEM AND PCR-BASED METHOD FOR IDENTIFICATION AND ANTIMICROBIAL SUSCEPTIBILITY PROFILE OF CLINICAL Enterococcus spp

    Science.gov (United States)

    Furlaneto-Maia, Luciana; Rocha, Kátia Real; Siqueira, Vera Lúcia Dias; Furlaneto, Márcia Cristina

    2014-01-01

    Enterococci are increasingly responsible for nosocomial infections worldwide. This study was undertaken to compare the identification and susceptibility profile using an automated MicrosScan system, PCR-based assay and disk diffusion assay of Enterococcus spp. We evaluated 30 clinical isolates of Enterococcus spp. Isolates were identified by MicrosScan system and PCR-based assay. The detection of antibiotic resistance genes (vancomycin, gentamicin, tetracycline and erythromycin) was also determined by PCR. Antimicrobial susceptibilities to vancomycin (30 µg), gentamicin (120 µg), tetracycline (30 µg) and erythromycin (15 µg) were tested by the automated system and disk diffusion method, and were interpreted according to the criteria recommended in CLSI guidelines. Concerning Enterococcus identification the general agreement between data obtained by the PCR method and by the automatic system was 90.0% (27/30). For all isolates of E. faecium and E. faecalis we observed 100% agreement. Resistance frequencies were higher in E. faecium than E. faecalis. The resistance rates obtained were higher for erythromycin (86.7%), vancomycin (80.0%), tetracycline (43.35) and gentamicin (33.3%). The correlation between disk diffusion and automation revealed an agreement for the majority of the antibiotics with category agreement rates of > 80%. The PCR-based assay, the van(A) gene was detected in 100% of vancomycin resistant enterococci. This assay is simple to conduct and reliable in the identification of clinically relevant enterococci. The data obtained reinforced the need for an improvement of the automated system to identify some enterococci. PMID:24626409

  16. The use of emotional intelligence capabilities in clinical reasoning and decision-making: A qualitative, exploratory study.

    Science.gov (United States)

    Hutchinson, Marie; Hurley, John; Kozlowski, Desirée; Whitehair, Leeann

    2018-02-01

    To explore clinical nurses' experiences of using emotional intelligence capabilities during clinical reasoning and decision-making. There has been little research exploring whether, or how, nurses employ emotional intelligence (EI) in clinical reasoning and decision-making. Qualitative phase of a larger mixed-methods study. Semistructured qualitative interviews with a purposive sample of registered nurses (n = 12) following EI training and coaching. Constructivist thematic analysis was employed to analyse the narrative transcripts. Three themes emerged: the sensibility to engage EI capabilities in clinical contexts, motivation to actively engage with emotions in clinical decision-making and incorporating emotional and technical perspectives in decision-making. Continuing to separate cognition and emotion in research, theorising and scholarship on clinical reasoning is counterproductive. Understanding more about nurses' use of EI has the potential to improve the calibre of decisions, and the safety and quality of care delivered. © 2017 John Wiley & Sons Ltd.

  17. Improving Decision Making about Genetic Testing in the Clinic: An Overview of Effective Knowledge Translation Interventions.

    Directory of Open Access Journals (Sweden)

    France Légaré

    Full Text Available Knowledge translation (KT interventions are attempts to change behavior in keeping with scientific evidence. While genetic tests are increasingly available to healthcare consumers in the clinic, evidence about their benefits is unclear and decisions about genetic testing are thus difficult for all parties.We sought to identify KT interventions that involved decisions about genetic testing in the clinical context and to assess their effectiveness for improving decision making in terms of behavior change, increased knowledge and wellbeing.We searched for trials assessing KT interventions in the context of genetic testing up to March 2014 in all systematic reviews (n = 153 published by two Cochrane review groups: Effective Practice and Organisation of Care (EPOC and Consumers and Communication.We retrieved 2473 unique trials of which we retained only 28 (1%. Two EPOC reviews yielded two trials of KT interventions: audit and feedback (n = 1 and educational outreach (n = 1. Both targeted health professionals and the KT intervention they assessed was found to be effective. Four Consumers and Communication reviews yielded 26 trials: decision aids (n = 15, communication of DNA-based disease risk estimates (n = 7, personalized risk communication (n = 3 and mobile phone messaging (n = 1. Among these, 25 trials targeted only health consumers or patients and the KT interventions were found to be effective in four trials, partly effective in seven, and ineffective in four. Lastly, only one trial targeted both physicians and patients and was found to be effective.More research on the effectiveness of KT interventions regarding genetic testing in the clinical context may contribute to patients making informed value-based decisions and drawing the maximum benefit from clinical applications of genetic and genomic innovations.

  18. Development of a clinical decision support system for diabetes care: A pilot study.

    Directory of Open Access Journals (Sweden)

    Livvi Li Wei Sim

    Full Text Available Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require

  19. Ictal SPECT using an attachable automated injector: clinical usefulness in the prediction of ictal onset zone.

    Science.gov (United States)

    Lee, Jung-Ju; Lee, Sang Kun; Choi, Jang Wuk; Kim, Dong-Wook; Park, Kyung Il; Kim, Bom Sahn; Kang, Hyejin; Lee, Dong Soo; Lee, Seo-Young; Kim, Sung Hun; Chung, Chun Kee; Nam, Hyeon Woo; Kim, Kwang Ki

    2009-12-01

    Ictal single-photon emission computed tomography (SPECT) is a valuable method for localizing the ictal onset zone in the presurgical evaluation of patients with intractable epilepsy. Conventional methods used to localize the ictal onset zone have problems with time lag from seizure onset to injection. To evaluate the clinical usefulness of a method that we developed, which involves an attachable automated injector (AAI), in reducing time lag and improving the ability to localize the zone of seizure onset. Patients admitted to the epilepsy monitoring unit (EMU) between January 1, 2003, and June 30, 2008, were included. The definition of ictal onset zone was made by comprehensive review of medical records, magnetic resonance imaging (MRI), data from video electroencephalography (EEG) monitoring, and invasive EEG monitoring if available. We comprehensively evaluated the time lag to injection and the image patterns of ictal SPECT using traditional visual analysis, statistical parametric mapping-assisted, and subtraction ictal SPECT coregistered to an MRI-assisted means of analysis. Image patterns were classified as localizing, lateralizing, and nonlateralizing. The whole number of patients was 99: 48 in the conventional group and 51 in the AAI group. The mean (SD) delay time to injection from seizure onset was 12.4+/-12.0 s in the group injected by our AAI method and 40.4+/-26.3 s in the group injected by the conventional method (P=0.000). The mean delay time to injection from seizure detection was 3.2+/-2.5 s in the group injected by the AAI method and 21.4+/-9.7 s in the group injected by the conventional method (P=0.000). The AAI method was superior to the conventional method in localizing the area of seizure onset (36 out of 51 with AAI method vs. 21 out of 48 with conventional method, P=0.009), especially in non-temporal lobe epilepsy (non-TLE) patients (17 out of 27 with AAI method vs. 3 out of 13 with conventional method, P=0.041), and in lateralizing the

  20. An engineered approach to stem cell culture: automating the decision process for real-time adaptive subculture of stem cells.

    Directory of Open Access Journals (Sweden)

    Dai Fei Elmer Ker

    Full Text Available Current cell culture practices are dependent upon human operators and remain laborious and highly subjective, resulting in large variations and inconsistent outcomes, especially when using visual assessments of cell confluency to determine the appropriate time to subculture cells. Although efforts to automate cell culture with robotic systems are underway, the majority of such systems still require human intervention to determine when to subculture. Thus, it is necessary to accurately and objectively determine the appropriate time for cell passaging. Optimal stem cell culturing that maintains cell pluripotency while maximizing cell yields will be especially important for efficient, cost-effective stem cell-based therapies. Toward this goal we developed a real-time computer vision-based system that monitors the degree of cell confluency with a precision of 0.791±0.031 and recall of 0.559±0.043. The system consists of an automated phase-contrast time-lapse microscope and a server. Multiple dishes are sequentially imaged and the data is uploaded to the server that performs computer vision processing, predicts when cells will exceed a pre-defined threshold for optimal cell confluency, and provides a Web-based interface for remote cell culture monitoring. Human operators are also notified via text messaging and e-mail 4 hours prior to reaching this threshold and immediately upon reaching this threshold. This system was successfully used to direct the expansion of a paradigm stem cell population, C2C12 cells. Computer-directed and human-directed control subcultures required 3 serial cultures to achieve the theoretical target cell yield of 50 million C2C12 cells and showed no difference for myogenic and osteogenic differentiation. This automated vision-based system has potential as a tool toward adaptive real-time control of subculturing, cell culture optimization and quality assurance/quality control, and it could be integrated with current and

  1. Automated closed-loop resuscitation of multiple hemorrhages: a comparison between fuzzy logic and decision table controllers in a sheep model.

    Science.gov (United States)

    Marques, Nicole Ribeiro; Ford, Brent J; Khan, Muzna N; Kinsky, Michael; Deyo, Donald J; Mileski, William J; Ying, Hao; Kramer, George C

    2017-01-01

    Hemorrhagic shock is the leading cause of trauma-related death in the military setting. Definitive surgical treatment of a combat casualty can be delayed and life-saving fluid resuscitation might be necessary in the field. Therefore, improved resuscitation strategies are critically needed for prolonged field and en route care. We developed an automated closed-loop control system capable of titrating fluid infusion to a target endpoint. We used the system to compare the performance of a decision table algorithm (DT) and a fuzzy logic controller (FL) to rescue and maintain the mean arterial pressure (MAP) at a target level during hemorrhages. Fuzzy logic empowered the control algorithm to emulate human expertise. We hypothesized that the FL controller would be more effective and more efficient than the DT algorithm by responding in a more rigid, structured way. Ten conscious sheep were submitted to a hemorrhagic protocol of 25 ml/kg over three separate bleeds. Automated resuscitation with lactated Ringer's was initiated 30 min after the first hemorrhage started. The endpoint target was MAP. Group differences were assessed by two-tailed t test and alpha of 0.05. Both groups maintained MAP at similar levels throughout the study. However, the DT group required significantly more fluid than the FL group, 1745 ± 552 ml (42 ± 11 ml/kg) versus 978 ± 397 ml (26 ± 11 ml/kg), respectively ( p  = 0.03). The FL controller was more efficient than the DT algorithm and may provide a means to reduce fluid loading. Effectiveness was not different between the two strategies. Automated closed-loop resuscitation can restore and maintain blood pressure in a multi-hemorrhage model of shock.

  2. Feminist poststructuralism: a methodological paradigm for examining clinical decision-making.

    Science.gov (United States)

    Arslanian-Engoren, Cynthia

    2002-03-01

    To present the philosophical framework of feminist poststructuralism, discuss its use as an innovative research approach and its implications for nursing knowledge development and practice. This perspective examines the construction of meaning, power relationships, and the importance of language as it affects contemporary healthcare decisions. It seeks to identify and expose biases that marginalize the healthcare needs of women and contribute to healthcare disparities for this population. Additionally, a feminist poststructuralist perspective seeks to develop new knowledge for understanding gender differences. A feminist poststructuralist perspective represents an alternative paradigm for studying the phenomenon of clinical decision-making. An empirical application example of a feminist poststructuralist perspective is provided. This exemplar investigated emergency department registered nurses' triage decisions for men and women with symptoms suggestive of coronary heart disease.

  3. Professional autonomy in 21st century healthcare: Nurses' accounts of clinical decision-making

    DEFF Research Database (Denmark)

    Traynor, Michael; Boland, Maggie; Buus, Niels

    2010-01-01

    profession for reasons including history, gender and a traditional subservience to medicine. This paper reports on a focus group study of UK nurses participating in post-qualifying professional development in 2008. Three groups of nurses in different specialist areas comprised a total of 26 participants....... The study uses accounts of decision-making to gain insight into contemporary professional nursing. The study also aims to explore the usefulness of a theory of professional work set out by Jamous and Peloille (1970). The analysis draws on notions of interpretive repertoires and elements of narrative...... analysis. We identified two interpretive repertoires: 'clinical judgement' which was used to describe the different grounds for making judgements; and 'decision-making' which was used to describe organisational circumstances influencing decision-making. Jamous and Peloille's theory proved useful...

  4. Clinical decision support software for diabetic foot risk stratification: development and formative evaluation.

    Science.gov (United States)

    Schoen, Deborah E; Glance, David G; Thompson, Sandra C

    2015-01-01

    Identifying people at risk of developing diabetic foot complications is a vital step in prevention programs in primary healthcare settings. Diabetic foot risk stratification systems predict foot ulceration. The aim of this study was to explore the views and experiences of potential end users during development and formative evaluations of an electronic diabetic foot risk stratification tool based on evidence-based guidelines and determine the accuracy of the tool. Formative evaluation of the risk tool occurred in five stages over an eight-month period and employed a mixed methods research design consisting of semi-structured interviews, focus group and participant observation, online survey, expert review, comparison to the Australian Guidelines and clinical testing. A total of 43 healthcare practitioners trialled the computerised clinical decision support system during development, with multiple software changes made as a result of feedback. Individual and focus group participants exposed critical design flaws. Live testing revealed risk stratification errors and functional limitations providing the basis for practical improvements. In the final product, all risk calculations and recommendations made by the clinical decision support system reflect current Australian Guidelines. Development of the computerised clinical decision support system using evidence-based guidelines can be optimised by a multidisciplinary iterative process of feedback, testing and software adaptation by experts in modern development technologies.

  5. Clinical Decision Making in the Management of Patients With Cervicogenic Dizziness: A Case Series.

    Science.gov (United States)

    Jung, Francis C; Mathew, Sherin; Littmann, Andrew E; MacDonald, Cameron W

    2017-11-01

    Study Design Case series. Background Although growing recognition of cervicogenic dizziness (CGD) is emerging, there is still no gold standard for the diagnosis of CGD. The purpose of this case series is to describe the clinical decision making utilized in the management of 7 patients presenting with CGD. Case Description Patients presenting with neck pain and accompanying subjective symptoms, including dizziness, unsteadiness, light-headedness, and visual disturbance, were selected. Clinical evidence of a temporal relationship between neck pain and dizziness, with or without sensorimotor disturbances, was assessed. Clinical decision making followed a 4-step process, informed by the current available best evidence. Outcome measures included the numeric rating scale for dizziness and neck pain, the Dizziness Handicap Inventory, Patient-Specific Functional Scale, and global rating of change. Outcomes Seven patients (mean age, 57 years; range, 31-86 years; 7 female) completed physical therapy management at an average of 13 sessions (range, 8-30 sessions) over a mean of 7 weeks. Clinically meaningful improvements were observed in the numeric rating scale for dizziness (mean difference, 5.7; 95% confidence interval [CI]: 4.0, 7.5), neck pain (mean difference, 5.4; 95% CI: 3.8, 7.1), and the Dizziness Handicap Inventory (mean difference, 32.6; 95% CI: 12.9, 52.2) at discontinuation. Patients also demonstrated overall satisfaction via the Patient-Specific Functional Scale (mean difference, 9) and global rating of change (mean, +6). Discussion This case series describes the physical therapist decision making, management, and outcomes in patients with CGD. Further investigation is warranted to develop a valid clinical decision-making guideline to inform management of patients with CGD. Level of Evidence Diagnosis, therapy, level 4. J Orthop Sports Phys Ther 2017;47(11):874-884. Epub 9 Oct 2017. doi:10.2519/jospt.2017.7425.

  6. How meta-analytic evidence impacts clinical decision making in oral implantology: a Delphi opinion poll.

    Science.gov (United States)

    Pommer, Bernhard; Becker, Kathrin; Arnhart, Christoph; Fabian, Ferenc; Rathe, Florian; Stigler, Robert G

    2016-03-01

    To investigate the impact of meta-analytic evidence in scientific literature on clinical decision making in the field of oral implantology. A Delphi opinion poll was performed at the meeting of the "Next Generation" Committees of the Austrian, German and Swiss Societies for Implantology (ÖGI, DGI and SGI). First, the experts gave their opinion on 20 questions regarding routine implant treatment (uninformed decisions), then they were confronted with up-to-date Level I evidence from scientific literature on these topics and again asked to give their opinion (informed decisions) as well as to rate the available evidence as satisfactory or insufficient. Topics involved surgical issues, such as immediate implant placement, flapless surgery, tilted and short implants and bone substitute materials, as well as opinions on prosthodontic paradigms, such as immediate loading, abutment materials and platform switching. Compared to their uninformed decisions prior to confrontation with recent scientific literature, on average, 37% of experts (range: 15-50%) changed their opinion on the topic. When originally favoring one treatment alternative, less than half were still convinced after review of meta-analytic evidence. Discrepancy between uninformed and informed decisions was significantly associated with insufficient evidence (P = 0.014, 49% change of opinion vs. 26% on topics rated as sufficiently backed with evidence). Agreement regarding strength of evidence could be reached for eight topics (40%), in three issues toward sufficiency and in five issues toward lack of evidence. Confrontation with literature results significantly changes clinical decisions of implantologists, particularly in cases of ambiguous or lacking meta-analytic evidence. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  7. Knowledge of Fecal Calprotectin and Infliximab Trough Levels Alters Clinical Decision-making for IBD Outpatients on Maintenance Infliximab Therapy.

    Science.gov (United States)

    Huang, Vivian W; Prosser, Connie; Kroeker, Karen I; Wang, Haili; Shalapay, Carol; Dhami, Neil; Fedorak, Darryl K; Halloran, Brendan; Dieleman, Levinus A; Goodman, Karen J; Fedorak, Richard N

    2015-06-01

    Infliximab is an effective therapy for inflammatory bowel disease (IBD). However, more than 50% of patients lose response. Empiric dose intensification is not effective for all patients because not all patients have objective disease activity or subtherapeutic drug level. The aim was to determine how an objective marker of disease activity or therapeutic drug monitoring affects clinical decisions regarding maintenance infliximab therapy in outpatients with IBD. Consecutive patients with IBD on maintenance infliximab therapy were invited to participate by providing preinfusion stool and blood samples. Fecal calprotectin (FCP) and infliximab trough levels (ITLs) were measured by enzyme linked immunosorbent assay. Three decisions were compared: (1) actual clinical decision, (2) algorithmic FCP or ITL decisions, and (3) expert panel decision based on (a) clinical data, (b) clinical data plus FCP, and (c) clinical data plus FCP plus ITL. In secondary analysis, Receiver-operating curves were used to assess the ability of FCP and ITL in predicting clinical disease activity or remission. A total of 36 sets of blood and stool were available for analysis; median FCP 191.5 μg/g, median ITLs 7.3 μg/mL. The actual clinical decision differed from the hypothetical decision in 47.2% (FCP algorithm); 69.4% (ITL algorithm); 25.0% (expert panel clinical decision); 44.4% (expert panel clinical plus FCP); 58.3% (expert panel clinical plus FCP plus ITL) cases. FCP predicted clinical relapse (area under the curve [AUC] = 0.417; 95% confidence interval [CI], 0.197-0.641) and subtherapeutic ITL (AUC = 0.774; 95% CI, 0.536-1.000). ITL predicted clinical remission (AUC = 0.498; 95% CI, 0.254-0.742) and objective remission (AUC = 0.773; 95% CI, 0.622-0.924). Using FCP and ITLs in addition to clinical data results in an increased number of decisions to optimize management in outpatients with IBD on stable maintenance infliximab therapy.

  8. A clinical decision support system for diagnosis of Allergic Rhinitis based on intradermal skin tests.

    Science.gov (United States)

    Jabez Christopher, J; Khanna Nehemiah, H; Kannan, A

    2015-10-01

    Allergic Rhinitis is a universal common disease, especially in populated cities and urban areas. Diagnosis and treatment of Allergic Rhinitis will improve the quality of life of allergic patients. Though skin tests remain the gold standard test for diagnosis of allergic disorders, clinical experts are required for accurate interpretation of test outcomes. This work presents a clinical decision support system (CDSS) to assist junior clinicians in the diagnosis of Allergic Rhinitis. Intradermal Skin tests were performed on patients who had plausible allergic symptoms. Based on patient׳s history, 40 clinically relevant allergens were tested. 872 patients who had allergic symptoms were considered for this study. The rule based classification approach and the clinical test results were used to develop and validate the CDSS. Clinical relevance of the CDSS was compared with the Score for Allergic Rhinitis (SFAR). Tests were conducted for junior clinicians to assess their diagnostic capability in the absence of an expert. The class based Association rule generation approach provides a concise set of rules that is further validated by clinical experts. The interpretations of the experts are considered as the gold standard. The CDSS diagnoses the presence or absence of rhinitis with an accuracy of 88.31%. The allergy specialist and the junior clinicians prefer the rule based approach for its comprehendible knowledge model. The Clinical Decision Support Systems with rule based classification approach assists junior doctors and clinicians in the diagnosis of Allergic Rhinitis to make reliable decisions based on the reports of intradermal skin tests. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Retention payoff-based cost per day open regression equations: Application in a user-friendly decision support tool for investment analysis of automated estrus detection technologies.

    Science.gov (United States)

    Dolecheck, K A; Heersche, G; Bewley, J M

    2016-12-01

    Assessing the economic implications of investing in automated estrus detection (AED) technologies can be overwhelming for dairy producers. The objectives of this study were to develop new regression equations for estimating the cost per day open (DO) and to apply the results to create a user-friendly, partial budget, decision support tool for investment analysis of AED technologies. In the resulting decision support tool, the end user can adjust herd-specific inputs regarding general management, current reproductive management strategies, and the proposed AED system. Outputs include expected DO, reproductive cull rate, net present value, and payback period for the proposed AED system. Utility of the decision support tool was demonstrated with an example dairy herd created using data from DairyMetrics (Dairy Records Management Systems, Raleigh, NC), Food and Agricultural Policy Research Institute (Columbia, MO), and published literature. Resulting herd size, rolling herd average milk production, milk price, and feed cost were 323 cows, 10,758kg, $0.41/kg, and $0.20/kg of dry matter, respectively. Automated estrus detection technologies with 2 levels of initial system cost (low: $5,000 vs. high: $10,000), tag price (low: $50 vs. high: $100), and estrus detection rate (low: 60% vs. high: 80%) were compared over a 7-yr investment period. Four scenarios were considered in a demonstration of the investment analysis tool: (1) a herd using 100% visual observation for estrus detection before adopting 100% AED, (2) a herd using 100% visual observation before adopting 75% AED and 25% visual observation, (3) a herd using 100% timed artificial insemination (TAI) before adopting 100% AED, and (4) a herd using 100% TAI before adopting 75% AED and 25% TAI. Net present value in scenarios 1 and 2 was always positive, indicating a positive investment situation. Net present value in scenarios 3 and 4 was always positive in combinations using a $50 tag price, and in scenario 4, the $5

  10. Replacing the mercury manometer with an oscillometric device in a hypertension clinic: implications for clinical decision making.

    Science.gov (United States)

    Stergiou, G S; Lourida, P; Tzamouranis, D

    2011-11-01

    Oscillometric devices are being widely used for ambulatory, home and office blood pressure (BP) measurement, and several of them have been validated using established protocols. This cross-sectional study assessed the impact on antihypertensive treatment decisions of replacing the mercury sphygmomanometer by a validated oscillometric device. Consecutive subjects attending a hypertension clinic had triplicate simultaneous same-arm BP measurements using a mercury sphygmomanometer and a validated professional oscillometric device. For each device, uncontrolled hypertension was defined as average BP ≥140/90 mm Hg (systolic/diastolic). A total of 5108 simultaneous BP measurements were obtained from 763 subjects in 1717 clinic visits. In 24% of all visits, the mercury and the oscillometric BP measurements led to different conclusion regarding the diagnosis of uncontrolled hypertension. In 4.9% of the visits, the diagnostic disagreement was considered as 'clinically important' (BP exceeding the diagnostic threshold by >5 mm Hg). These data suggest that the replacement of the mercury sphygmomanometer by a validated professional oscillometric device will result into different treatment decisions in about 5% of the cases. Therefore, and because of the known problems when using mercury devices and the auscultatory technique in clinical practise, the oscillometric devices are regarded as reliable alternatives to the mercury sphygmomanometer for office use.

  11. Clinical judgment and decision making in wound assessment and management: is experience enough?

    Science.gov (United States)

    Logan, Gemma

    2015-03-01

    The assessment and management of wounds forms a large proportion of community nurses' workload, often requiring judgment and decision-making in complex, challenging and uncertain circumstances. The processes through which nurses form judgments and make decisions within this context are reviewed in this article against existing theories on these on these subjects. There is variability in wound assessment and management practice which may be attributed to uncertainties within the context, a lack of knowledge in appropriate treatment choices and the inability to correctly value the importance of the clinical information presented. Nurses may be required to draw on intuition to guide their judgments and decision-making by association with experience and expertise. In addition, a step-by-step analytical approach underpinned by an evidence base may be required to ensure accuracy in practice. Developing an understanding of the different theories of judgment and decision-making may facilitate nurses' abilities to reflect on their own decision tasks, thereby enhancing the care provided.

  12. Clinical decision support systems in hospital care using ubiquitous devices: Current issues and challenges.

    Science.gov (United States)

    Baig, Mirza Mansoor; GholamHosseini, Hamid; Moqeem, Aasia A; Mirza, Farhaan; Lindén, Maria

    2017-11-01

    Supporting clinicians in decision making using advanced technologies has been an active research area in biomedical engineering during the past years. Among a wide range of ubiquitous systems, smartphone applications have been increasingly developed in healthcare settings to help clinicians as well as patients. Today, many smartphone applications, from basic data analysis to advanced patient monitoring, are available to clinicians and patients. Such applications are now increasingly integrating into healthcare for clinical decision support, and therefore, concerns around accuracy, stability, and dependency of these applications are rising. In addition, lack of attention to the clinicians' acceptability, as well as the low impact on the medical professionals' decision making, are posing more serious issues on the acceptability of smartphone applications. This article reviews smartphone-based decision support applications, focusing on hospital care settings and their overall impact of these applications on the wider clinical workflow. Additionally, key challenges and barriers of the current ubiquitous device-based healthcare applications are identified. Finally, this article addresses current challenges, future directions, and the adoption of mobile healthcare applications.

  13. A serious game can be a valid method to train clinical decision-making in surgery.

    Science.gov (United States)

    Graafland, Maurits; Vollebergh, Maarten F; Lagarde, Sjoerd M; van Haperen, M; Bemelman, Willem A; Schijven, Marlies P

    2014-12-01

    A serious game was developed to train surgical residents in clinical decision-making regarding biliary tract disease. Serious or applied gaming is a novel educational approach to postgraduate training, combining training and assessment of clinical decision-making in a fun and challenging way. Although interest for serious games in medicine is rising, evidence on its validity is lacking. This study investigates face, content, and construct validity of this serious game. Experts structurally validated the game's medical content. Subsequently, 41 participants played the game. Decision scores and decision speed were compared among surgeons, surgical residents, interns, and medical students, determining the game's discriminatory ability between different levels of expertise. After playing, participants completed a questionnaire on the game's perceived realism and teaching ability. Surgeons solved more cases correctly (mean 77 %) than surgical residents (67 %), interns (60 %), master-degree students (50 %), and bachelor-degree students (39 % (p educators and surgical trainees found the game both realistic and useful for surgical training. The majority perceived the game as fun (91.2 %), challenging (85.3 %), and would recommend the game to educate their colleagues (81.8 %). This serious game showed clear discriminatory ability between different levels of expertise in biliary tract disease management and clear teaching capability. It was perceived as appealing and realistic. Serious gaming has the potential to increase adherence to training programs in surgical residency training and medical school.

  14. Unconscious race and social class bias among acute care surgical clinicians and clinical treatment decisions.

    Science.gov (United States)

    Haider, Adil H; Schneider, Eric B; Sriram, N; Dossick, Deborah S; Scott, Valerie K; Swoboda, Sandra M; Losonczy, Lia; Haut, Elliott R; Efron, David T; Pronovost, Peter J; Lipsett, Pamela A; Cornwell, Edward E; MacKenzie, Ellen J; Cooper, Lisa A; Freischlag, Julie A

    2015-05-01

    Significant health inequities persist among minority and socially disadvantaged patients. Better understanding of how unconscious biases affect clinical decision making may help to illuminate clinicians' roles in propagating disparities. To determine whether clinicians' unconscious race and/or social class biases correlate with patient management decisions. We conducted a web-based survey among 230 physicians from surgery and related specialties at an academic, level I trauma center from December 1, 2011, through January 31, 2012. We administered clinical vignettes, each with 3 management questions. Eight vignettes assessed the relationship between unconscious bias and clinical decision making. We performed ordered logistic regression analysis on the Implicit Association Test (IAT) scores and used multivariable analysis to determine whether implicit bias was associated with the vignette responses. Differential response times (D scores) on the IAT as a surrogate for unconscious bias. Patient management vignettes varied by patient race or social class. Resulting D scores were calculated for each management decision. In total, 215 clinicians were included and consisted of 74 attending surgeons, 32 fellows, 86 residents, 19 interns, and 4 physicians with an undetermined level of education. Specialties included surgery (32.1%), anesthesia (18.1%), emergency medicine (18.1%), orthopedics (7.9%), otolaryngology (7.0%), neurosurgery (7.0%), critical care (6.0%), and urology (2.8%); 1.9% did not report a departmental affiliation. Implicit race and social class biases were present in most respondents. Among all clinicians, mean IAT D scores for race and social class were 0.42 (95% CI, 0.37-0.48) and 0.71 (95% CI, 0.65-0.78), respectively. Race and class scores were similar across departments (general surgery, orthopedics, urology, etc), race, or age. Women demonstrated less bias concerning race (mean IAT D score, 0.39 [95% CI, 0.29-0.49]) and social class (mean IAT D score

  15. Ethnic bias and clinical decision-making among New Zealand medical students: an observational study.

    Science.gov (United States)

    Harris, Ricci; Cormack, Donna; Stanley, James; Curtis, Elana; Jones, Rhys; Lacey, Cameron

    2018-01-23

    Health professional racial/ethnic bias may impact on clinical decision-making and contribute to subsequent ethnic health inequities. However, limited research has been undertaken among medical students. This paper presents findings from the Bias and Decision-Making in Medicine (BDMM) study, which sought to examine ethnic bias (Māori (indigenous peoples) compared with New Zealand European) among medical students and associations with clinical decision-making. All final year New Zealand (NZ) medical students in 2014 and 2015 (n = 888) were invited to participate in a cross-sectional online study. Key components included: two chronic disease vignettes (cardiovascular disease (CVD) and depression) with randomized patient ethnicity (Māori or NZ European) and questions on patient management; implicit bias measures (an ethnicity preference Implicit Association Test (IAT) and an ethnicity and compliant patient IAT); and, explicit ethnic bias questions. Associations between ethnic bias and clinical decision-making responses to vignettes were tested using linear regression. Three hundred and two students participated (34% response rate). Implicit and explicit ethnic bias favoring NZ Europeans was apparent among medical students. In the CVD vignette, no significant differences in clinical decision-making by patient ethnicity were observed. There were also no differential associations by patient ethnicity between any measures of ethnic bias (implicit or explicit) and patient management responses in the CVD vignette. In the depression vignette, some differences in the ranking of recommended treatment options were observed by patient ethnicity and explicit preference for NZ Europeans was associated with increased reporting that NZ European patients would benefit from treatment but not Māori (slope difference 0.34, 95% CI 0.08, 0.60; p = 0.011), although this was the only significant finding in these analyses. NZ medical students demonstrated ethnic bias, although

  16. Newly Graduated Nurses' use of Knowledge Sources in Clinical Decision Making

    DEFF Research Database (Denmark)

    Lygum Voldbjerg, Siri

    -based practice. It also underlines a need for a greater concurrence on how knowledge sources are articulated and used in nursing education and clinical practice. This study may inform interventions as to how newly graduated nurses can be supported in their use of knowledge, skills and competencies within......Evidence-based practice has been introduced internationally as a standard for healthcare delivery to improve the quality of care, thus ensuring safe care and treatment. Evidence-based practice calls for a decision-making that specifically requires nurses to place the patient at the centre...... of clinical decisions, based on transparent, articulate and reflective use of knowledge sources. Furthermore, it is implied that nurses are able to retrieve, asses, implement and evaluate research evidence. To meet these requirements, nursing educations around the world have organised curricula to educate...

  17. How Qualitative Research Informs Clinical and Policy Decision Making in Transplantation: A Review.

    Science.gov (United States)

    Tong, Allison; Morton, Rachael L; Webster, Angela C

    2016-09-01

    Patient-centered care is no longer just a buzzword. It is now widely touted as a cornerstone in delivering quality care across all fields of medicine. However, patient-centered strategies and interventions necessitate evidence about patients' decision-making processes, values, priorities, and needs. Qualitative research is particularly well suited to understanding the experience and perspective of patients, donors, clinicians, and policy makers on a wide range of transplantation-related topics including organ donation and allocation, adherence to prescribed therapy, pretransplant and posttransplant care, implementation of clinical guidelines, and doctor-patient communication. In transplantation, evidence derived from qualitative research has been integrated into strategies for shared decision-making, patient educational resources, process evaluations of trials, clinical guidelines, and policies. The aim of this article is to outline key concepts and methods used in qualitative research, guide the appraisal of qualitative studies, and assist clinicians to understand how qualitative research may inform their practice and policy.

  18. Clinical trial or standard treatment? Shared decision making at the department of oncology

    DEFF Research Database (Denmark)

    Gregersen, Trine Ammentorp; Birkelund, Regner; Ammentorp, Jette

    2016-01-01

    Title: Clinical trial or standard treatment? Shared decision making at the department of oncology. Authors: Ph.d. student, Trine A. Gregersen. Trine.gregersen@rsyd.dk. Department of Oncology. Health Services Research Unit Lillebaelt Hospital / IRS University of Southern Denmark. Professor, Regner...... Analyzing field notes: • How to write useful field notes? • How to analyze field notes systematically? • Using Nvivo when analyzing field notes and interviews?...

  19. Access to augmentative and alternative communication: new technologies and clinical decision-making.

    Science.gov (United States)

    Fager, Susan; Bardach, Lisa; Russell, Susanne; Higginbotham, Jeff

    2012-01-01

    Children with severe physical impairments require a variety of access options to augmentative and alternative communication (AAC) and computer technology. Access technologies have continued to develop, allowing children with severe motor control impairments greater independence and access to communication. This article will highlight new advances in access technology, including eye and head tracking, scanning, and access to mainstream technology, as well as discuss future advances. Considerations for clinical decision-making and implementation of these technologies will be presented along with case illustrations.

  20. Physicians' perspectives on communication and decision making in clinical encounters for treatment of latent tuberculosis infection

    OpenAIRE

    Claudia C. Dobler; Sinthia Bosnic-Anticevich; Carol L. Armour

    2018-01-01

    The aim of the study was to explore the views of tuberculosis (TB) physicians on treatment of latent TB infection (LTBI), focusing on decision making and communication in clinical practice. 20 Australian TB physicians participated in a semistructured interview in person or over the telephone. Interviews were recorded, transcribed and analysed thematically. The study identified challenges that physicians face when discussing treatment for LTBI with patients. These included difficulties explain...

  1. Incorporating patient decision aids into standard clinical practice in an integrated delivery system.

    Science.gov (United States)

    Hsu, Clarissa; Liss, David T; Westbrook, Emily O; Arterburn, David

    2013-01-01

    Randomized controlled trials show that patient decision aids (DAs) can promote shared decision making and improve decision quality. Despite this evidence, integration of DAs into routine clinical practice has proceeded slowly. To identify factors that promote or impede integrating DAs into clinical practice in a large health care delivery system. Mixed-methods case study. Group Health, an integrated health plan and care delivery system in Washington state. Intervention. The project was carried out in 6 specialty service lines using 12 video-based DAs for preference-sensitive conditions related to elective surgical procedures. Process data, site visits, meeting observations, and in-depth interviews conducted with clinical staff, project staff, and health plan leaders in 2009 and 2010. The project established systemwide and clinic-specific processes that facilitated the distribution of approximately 10,000 DAs over 2 years. Several factors were identified as important for success in this implementation, including strong support from senior leaders, establishing a system for previsit ordering and providing timely feedback to teams about distribution rates, engaging providers and staff in development of the implementation process, and finding ways to address concerns about conditions that were perceived as life-threatening and/or time sensitive. Limitations included lack of data on patient perspectives, an implementation setting with salaried providers, and frontline provider interviews conducted in only selected service lines. With strong leadership, financial support, and a well-defined implementation strategy, 12 video-based DAs in 6 specialty service lines were integrated into routine practice over 2 years. Findings from this demonstration may advance the ability of other organizations to use DAs effectively and promote widespread adoption of shared decision making in routine patient care.

  2. The utility of observational studies in clinical decision making: lessons learned from statin trials.

    Science.gov (United States)

    Foody, JoAnne M; Mendys, Phillip M; Liu, Larry Z; Simpson, Ross J

    2010-05-01

    Contemporary clinical decision making is well supported by a wide variety of information sources, including clinical practice guidelines, position papers, and insights from randomized controlled trials (RCTs). Much of our fundamental understanding of cardiovascular risk factors is based on multiple observations from major epidemiologic studies, such as The Seven Country Studies and the US-based Framingham Heart Study. These studies provided the framework for the development of clinical practice guidelines, including the National Cholesterol Education Program Adult Treatment Panel series. The objective of this article is to highlight the value of observational studies as a complement to clinical trial data for clinical decision making in real-world practice. Although RCTs are still the benchmark for assessing clinical efficacy and safety of a specific therapeutic approach, they may be of limited utility to practitioners who must then adapt the lessons learned from the trial into the patient care environment. The use of well-structured observational studies can improve our understanding of the translation of clinical trials into clinical practice, as demonstrated here with the example of statins. Although such studies have their own limitations, improved techniques for design and analysis have reduced the impact of bias and confounders. The introduction of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines has provided more uniformity for such studies. When used together with RCTs, observational studies can enhance our understanding of effectiveness and utility in real-world clinical practice. In the examples of statin observational studies, the results suggest that relative effectiveness of different statins and potential impact of switching statins should be carefully considered in treating individual patients by practicing physicians.

  3. Nurses' pressure ulcer related judgements and decisions in clinical practice: a systematic review.

    Science.gov (United States)

    Samuriwo, Ray; Dowding, Dawn

    2014-12-01

    Pressure ulcers are considered to be an adverse outcome of care that should never occur in clinical practice. The formation of a pressure ulcer is also perceived to be an indicator of poor quality nursing care. Therefore, pressure ulcer prevention is a priority for nurses, healthcare professionals and healthcare organisations throughout the world. A key factor in pressure ulcer prevention and management is individual nurse decision making. To synthesise the literature on the judgement and decision making of nurses in relation to the assessment, prevention, grading and management of pressure ulcers in all care settings (hospital and community). A systematic search of published literature relating to judgement and decision making in nurses, with a focus on the prevention and management of pressure ulcers. A search of electronic databases from 1992 to present, together with hand searching of the reference lists of retrieved publications, to identify published papers that reported results of studies evaluating the decision making of nurses in relation to the prevention and management of pressure ulcers. Abstracts were independently reviewed by two authors and full text of potentially relevant articles retrieved. Each paper included in this systematic review was evaluated using recognised appraisal criteria relevant to the specific study design. Included papers provided empirical data on key aspects of nurses' pressure ulcer related judgements and decision making. Data were synthesised into themes using narrative analysis. Sixteen studies and one systematic review were included in the review, focusing on pressure ulcer risk assessment, pressure ulcer prevention, grading of pressure ulcers and treatment decisions. The results indicated that assessment tools were not routinely used to identify pressure ulcer risk, and that nurses rely on their own knowledge and experience rather than research evidence to decide what skin care to deliver. Emphasising pressure ulcer risk

  4. PCA safety data review after clinical decision support and smart pump technology implementation.

    Science.gov (United States)

    Prewitt, Judy; Schneider, Susan; Horvath, Monica; Hammond, Julia; Jackson, Jason; Ginsberg, Brian

    2013-06-01

    Medication errors account for 20% of medical errors in the United States with the largest risk at prescribing and administration. Analgesics or opioids are frequently used medications that can be associated with patient harm when prescribed or administered improperly. In an effort to decrease medication errors, Duke University Hospital implemented clinical decision support via computer provider order entry (CPOE) and "smart pump" technology, 2/2008, with the goal to decrease patient-controlled analgesia (PCA) adverse events. This project evaluated PCA safety events, reviewing voluntary report system and adverse drug events via surveillance (ADE-S), on intermediate and step-down units preimplementation and postimplementation of clinical decision support via CPOE and PCA smart pumps for the prescribing and administration of opioids therapy in the adult patient requiring analgesia for acute pain. Voluntary report system and ADE-S PCA events decreased based upon 1000 PCA days; ADE-S PCA events per 1000 PCA days decreased 22%, from 5.3 (pre) to 4.2 (post) (P = 0.09). Voluntary report system events decreased 72%, from 2.4/1000 PCA days (pre) to 0.66/1000 PCA days (post) and was statistically significant (P PCA events between time periods in both the ADE-S and voluntary report system data, thus supporting the recommendation of clinical decision support via CPOE and PCA smart pump technology.

  5. Informational resources utilized in clinical decision making: common practices in dentistry.

    Science.gov (United States)

    Straub-Morarend, Cheryl L; Marshall, Teresa A; Holmes, David C; Finkelstein, Michael W

    2011-04-01

    This study investigated current trends of Iowa dental practitioners with regard to acquisition and utilization of scientific information resources to support decision making in the clinical practice of dentistry. A survey questionnaire regarding the utilization of various sources of information to support clinical decisions was mailed in September 2009 to all dentists licensed and practicing in the state of Iowa. Dentists appointed full-time within the University of Iowa College of Dentistry were excluded from this study. Continuing education courses were the most frequently utilized and preferred information source by respondents, followed by print journals and consultation with other health care professionals. Practice patterns according to decade of dental school graduation as well as scope of practice were noted. The results of this study demonstrate that dental practitioners utilize a variety of evidence-based and non-evidence-based information resources to support decisions in clinical practice. The habits of newer graduates vary somewhat from those of earlier graduates; the habits of specialists vary from those of general practitioners.

  6. The potential of predictive analytics to provide clinical decision support in depression treatment planning.

    Science.gov (United States)

    Kessler, Ronald C

    2018-01-01

    To review progress developing clinical decision support tools for personalized treatment of major depressive disorder (MDD). Over the years, a variety of individual indicators ranging from biomarkers to clinical observations and self-report scales have been used to predict various aspects of differential MDD treatment response. Most of this work focused on predicting remission either with antidepressant medications versus psychotherapy, some antidepressant medications versus others, some psychotherapies versus others, and combination therapies versus monotherapies. However, to date, none of the individual predictors in these studies has been strong enough to guide optimal treatment selection for most patients. Interest consequently turned to decision support tools made up of multiple predictors, but the development of such tools has been hampered by small study sample sizes. Design recommendations are made here for future studies to address this problem. Recommendations include using large prospective observational studies followed by pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary development of decision support tools; basing these tools on comprehensive batteries of inexpensive self-report and clinical predictors (e.g., self-administered performance-based neurocognitive tests) versus expensive biomarkers; and reserving biomarker assessments for targeted studies of patients not well classified by inexpensive predictor batteries.

  7. Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support.

    Science.gov (United States)

    Zhou, Xuezhong; Chen, Shibo; Liu, Baoyan; Zhang, Runsun; Wang, Yinghui; Li, Ping; Guo, Yufeng; Zhang, Hua; Gao, Zhuye; Yan, Xiufeng

    2010-01-01

    Traditional Chinese medicine (TCM) is a scientific discipline, which develops the related theories from the long-term clinical practices. The large-scale clinical data are the core empirical knowledge source for TCM research. This paper introduces a clinical data warehouse (CDW) system, which incorporates the structured electronic medical record (SEMR) data for medical knowledge discovery and TCM clinical decision support (CDS). We have developed the clinical reference information model (RIM) and physical data model to manage the various information entities and their relationships in TCM clinical data. An extraction-transformation-loading (ETL) tool is implemented to integrate and normalize the clinical data from different operational data sources. The CDW includes online analytical processing (OLAP) and complex network analysis (CNA) components to explore the various clinical relationships. Furthermore, the data mining and CNA methods are used to discover the valuable clinical knowledge from the data. The CDW has integrated 20,000 TCM inpatient data and 20,000 outpatient data, which contains manifestations (e.g. symptoms, physical examinations and laboratory test results), diagnoses and prescriptions as the main information components. We propose a practical solution to accomplish the large-scale clinical data integration and preprocessing tasks. Meanwhile, we have developed over 400 OLAP reports to enable the multidimensional analysis of clinical data and the case-based CDS. We have successfully conducted several interesting data mining applications. Particularly, we use various classification methods, namely support vector machine, decision tree and Bayesian network, to discover the knowledge of syndrome differentiation. Furthermore, we have applied association rule and CNA to extract the useful acupuncture point and herb combination patterns from the clinical prescriptions. A CDW system consisting of TCM clinical RIM, ETL, OLAP and data mining as the core

  8. Clinical decision support improves quality of telephone triage documentation - an analysis of triage documentation before and after computerized clinical decision support

    Science.gov (United States)

    2014-01-01

    Background Clinical decision support (CDS) has been shown to be effective in improving medical safety and quality but there is little information on how telephone triage benefits from CDS. The aim of our study was to compare triage documentation quality associated with the use of a clinical decision support tool, ExpertRN©. Methods We examined 50 triage documents before and after a CDS tool was used in nursing triage. To control for the effects of CDS training we had an additional control group of triage documents created by nurses who were trained in the CDS tool, but who did not use it in selected notes. The CDS intervention cohort of triage notes was compared to both the pre-CDS notes and the CDS trained (but not using CDS) cohort. Cohorts were compared using the documentation standards of the American Academy of Ambulatory Care Nursing (AAACN). We also compared triage note content (documentation of associated positive and negative features relating to the symptoms, self-care instructions, and warning signs to watch for), and documentation defects pertinent to triage safety. Results Three of five AAACN documentation standards were significantly improved with CDS. There was a mean of 36.7 symptom features documented in triage notes for the CDS group but only 10.7 symptom features in the pre-CDS cohort (p triage note documentation quality. CDS-aided triage notes had significantly more information about symptoms, warning signs and self-care. The changes in triage documentation appeared to be the result of the CDS alone and not due to any CDS training that came with the CDS intervention. Although this study shows that CDS can improve documentation, further study is needed to determine if it results in improved care. PMID:24645674

  9. Automated population of an i2b2 clinical data warehouse from an openEHR-based data repository.

    Science.gov (United States)

    Haarbrandt, Birger; Tute, Erik; Marschollek, Michael

    2016-10-01

    Detailed Clinical Model (DCM) approaches have recently seen wider adoption. More specifically, openEHR-based application systems are now used in production in several countries, serving diverse fields of application such as health information exchange, clinical registries and electronic medical record systems. However, approaches to efficiently provide openEHR data to researchers for secondary use have not yet been investigated or established. We developed an approach to automatically load openEHR data instances into the open source clinical data warehouse i2b2. We evaluated query capabilities and the performance of this approach in the context of the Hanover Medical School Translational Research Framework (HaMSTR), an openEHR-based data repository. Automated creation of i2b2 ontologies from archetypes and templates and the integration of openEHR data instances from 903 patients of a paediatric intensive care unit has been achieved. In total, it took an average of ∼2527s to create 2.311.624 facts from 141.917 XML documents. Using the imported data, we conducted sample queries to compare the performance with two openEHR systems and to investigate if this representation of data is feasible to support cohort identification and record level data extraction. We found the automated population of an i2b2 clinical data warehouse to be a feasible approach to make openEHR data instances available for secondary use. Such an approach can facilitate timely provision of clinical data to researchers. It complements analytics based on the Archetype Query Language by allowing querying on both, legacy clinical data sources and openEHR data instances at the same time and by providing an easy-to-use query interface. However, due to different levels of expressiveness in the data models, not all semantics could be preserved during the ETL process. Copyright © 2016 Elsevier Inc. All rights reserved.

  10. Can a patient smart card improve decision making in a clinical setting?.

    Science.gov (United States)

    Bérubé, J; Papillon, M J; Lavoie, G; Durant, P; Fortin, J P

    1995-01-01

    In the health field, clinical information is the raw material for the clinician delivering health services. Therefore, the clinical information available to the physician is often incomplete or even non¿existent upon consultation. Furthermore, the reconstruction of the medical history, which is the most important source of data for the clinician to establish a diagnosis and initiate a treatment, suffers from many constraints. The smart card, like the one used in Quebec's project, could ease the physician's decision-making by allowing fast access to accurate and pertinent data. The smart card is a major asset in the present health system.

  11. Clinical decision-making: heuristics and cognitive biases for the ophthalmologist.

    Science.gov (United States)

    Hussain, Ahsen; Oestreicher, James

    Diagnostic errors have a significant impact on health care outcomes and patient care. The underlying causes and development of diagnostic error are complex with flaws in health care systems, as well as human error, playing a role. Cognitive biases and a failure of decision-making shortcuts (heuristics) are human factors that can compromise the diagnostic process. We describe these mechanisms, their role with the clinician, and provide clinical scenarios to highlight the various points at which biases may emerge. We discuss strategies to modify the development and influence of these processes and the vulnerability of heuristics to provide insight and improve clinical outcomes. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. A programmable rules engine to provide clinical decision support using HTML forms.

    Science.gov (United States)

    Heusinkveld, J; Geissbuhler, A; Sheshelidze, D; Miller, R

    1999-01-01

    The authors have developed a simple method for specifying rules to be applied to information on HTML forms. This approach allows clinical experts, who lack the programming expertise needed to write CGI scripts, to construct and maintain domain-specific knowledge and ordering capabilities within WizOrder, the order-entry and decision support system used at Vanderbilt Hospital. The clinical knowledge base maintainers use HTML editors to create forms and spreadsheet programs for rule entry. A test environment has been developed which uses Netscape to display forms; the production environment displays forms using an embedded browser.

  13. Trail Blazing or Jam Session? Towards a new Concept of Clinical Decision-Making

    OpenAIRE

    Risør, Torsten

    2016-01-01

    Manuscript. Published version available at http://dx.doi.org/10.1080/13648470.2016.1239695 Clinical decision-making (CDM) is key in learning to be a doctor as the defining activity in their clinical work. CDM is often portrayed in the literature as similar to ‘trail blazing’; the doctor as the core agent, clearing away obstacles on the path towards diagnosis and treatment. However, in a fieldwork of young doctors in Denmark, it was difficult connect their practice to this image....

  14. A First Step towards a Clinical Decision Support System for Post-traumatic Stress Disorders.

    Science.gov (United States)

    Ma, Sisi; Galatzer-Levy, Isaac R; Wang, Xuya; Fenyö, David; Shalev, Arieh Y

    2016-01-01

    PTSD is distressful and debilitating, following a non-remitting course in about 10% to 20% of trauma survivors. Numerous risk indicators of PTSD have been identified, but individual level prediction remains elusive. As an effort to bridge the gap between scientific discovery and practical application, we designed and implemented a clinical decision support pipeline to provide clinically relevant recommendation for trauma survivors. To meet the specific challenge of early prediction, this work uses data obtained within ten days of a traumatic event. The pipeline creates personalized predictive model for each individual, and computes quality metrics for each predictive model. Clinical recommendations are made based on both the prediction of the model and its quality, thus avoiding making potentially detrimental recommendations based on insufficient information or suboptimal model. The current pipeline outperforms the acute stress disorder, a commonly used clinical risk factor for PTSD development, both in terms of sensitivity and specificity.

  15. Considerations for automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure.

    Science.gov (United States)

    Orlenko, Alena; Moore, Jason H; Orzechowski, Patryk; Olson, Randal S; Cairns, Junmei; Caraballo, Pedro J; Weinshilboum, Richard M; Wang, Liewei; Breitenstein, Matthew K

    2018-01-01

    With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic profiling endeavors, where potentially thousands of independent data points must be evaluated. In previous research, AutoML using high-dimensional data of varying types has been demonstrably robust, outperforming traditional approaches. However, considerations for application in clinical metabolic profiling remain to be evaluated. Particularly, regarding the robustness of AutoML to identify and adjust for common clinical confounders. In this study, we present a focused case study regarding AutoML considerations for using the Tree-Based Optimization Tool (TPOT) in metabolic profiling of exposure to metformin in a biobank cohort. First, we propose a tandem rank-accuracy measure to guide agnostic feature selection and corresponding threshold determination in clinical metabolic profiling endeavors. Second, while AutoML, using default parameters, demonstrated potential to lack sensitivity to low-effect confounding clinical covariates, we demonstrated residual training and adjustment of metabolite features as an easily applicable approach to ensure AutoML adjustment for potential confounding characteristics. Finally, we present increased homocysteine with long-term exposure to metformin as a potentially novel, non-replicated metabolite association suggested by TPOT; an association not identified in parallel clinical metabolic profiling endeavors. While warranting independent replication, our tandem rank-accuracy measure suggests homocysteine to be the metabolite feature with largest effect, and corresponding priority for further translational clinical research. Residual training and adjustment for a potential confounding effect by BMI only slightly modified

  16. Professional autonomy in 21st century healthcare: nurses' accounts of clinical decision-making.

    Science.gov (United States)

    Traynor, Michael; Boland, Maggie; Buus, Niels

    2010-10-01

    Autonomy in decision-making has traditionally been described as a feature of professional work, however the work of healthcare professionals has been seen as steadily encroached upon by State and managerialist forces. Nursing has faced particular problems in establishing itself as a credible profession for reasons including history, gender and a traditional subservience to medicine. This paper reports on a focus group study of UK nurses participating in post-qualifying professional development in 2008. Three groups of nurses in different specialist areas comprised a total of 26 participants. The study uses accounts of decision-making to gain insight into contemporary professional nursing. The study also aims to explore the usefulness of a theory of professional work set out by Jamous and Peloille (1970). The analysis draws on notions of interpretive repertoires and elements of narrative analysis. We identified two interpretive repertoires: 'clinical judgement' which was used to describe the different grounds for making judgements; and 'decision-making' which was used to describe organisational circumstances influencing decision-making. Jamous and Peloille's theory proved useful for interpreting instances where the nurses collectively withdrew from the potential dangers of too extreme claims for technicality or indeterminacy in their work. However, their theory did not explain the full range of accounts of decision-making that were given. Taken at face value, the accounts from the participants depict nurses as sometimes practising in indirect ways in order to have influence in the clinical and bureaucratic setting. However, a focus on language use and in particular, interpretive repertoires, has enabled us to suggest that despite an overall picture of severely limited autonomy, nurses in the groups reproduced stories of the successful accomplishment of moral and influential action. Copyright © 2010 Elsevier Ltd. All rights reserved.

  17. A Web-based clinical decision support system for depression care management.

    Science.gov (United States)

    Fortney, John C; Pyne, Jeffrey M; Steven, Christopher A; Williams, J Silas; Hedrick, Richard G; Lunsford, Amanda K; Raney, William N; Ackerman, Betty A; Ducker, Loretta O; Bonner, Laura M; Smith, Jeffrey L

    2010-11-01

    To inform the design of future informatics systems that support the chronic care model. We describe the development and functionality of a decision support system for the chronic care model of depression treatment, known as collaborative care. Dissemination of evidence-based collaborative care models has been slow, and fidelity to the evidence base has been poor during implementation initiatives. Implementation could be facilitated by a decision support system for depression care managers, the cornerstone of the collaborative care model. The Net Decision Support System (https://www.netdss.net/) is a free Web-based system that was developed to support depression care manager activities and to facilitate the dissemination of collaborative care models that maintain high fidelity to the evidence base. The NetDSS was based on intervention materials used for a randomized trial of depression care management that improved clinical outcomes compared with usual care. The NetDSS was developed jointly by a cross-functional design team of psychiatrists, depression care managers, information technology specialists, technical writers, and researchers. The NetDSS has the following functional capabilities: patient registry, patient encounter scheduler, trial management, clinical decision support, progress note generator, and workload and outcomes report generator. The NetDSS guides the care manager through a self-documenting patient encounter using evidence-based scripts and self-scoring instruments. The NetDSS has been used to provide evidence-based depression care management to more than 1700 primary care patients. Intervention protocols can be successfully converted to Web-based decision support systems that facilitate the implementation of evidence-based chronic care models into routine care with high fidelity.

  18. A Qualitative Study of the Influences on Clinical Academic Physicians' Postdoctoral Career Decision-Making.

    Science.gov (United States)

    Ranieri, Veronica F; Barratt, Helen; Rees, Geraint; Fulop, Naomi J

    2018-01-23

    To describe the influences on clinical academic physicians' postdoctoral career decision-making. Thirty-five doctoral trainee physicians from University College London took part in semi-structured interviews in 2015 and 2016. Participants were asked open-ended questions about their career to-date, their experiences undertaking a PhD, and their career plans post-PhD. The interviews were audio-recorded and transcribed. Thematic analysis was used to generate, review, and define themes from the transcripts. Emerging differences and similarities in participants' reasons for pursuing a PhD were then grouped to produce typologies to explore how their experiences influenced their career decision-making. Participants described four key reasons for undertaking a PhD, which formed the basis of the four typologies identified. These reasons included: to pursue a clinical academic career; to complete an extensive period of research to understand whether a clinical academic career was the desired path forward; to improve clinical career prospects; and to take a break from clinical training. These findings highlight the need to target efforts at retaining clinical academic physicians according to their reasons for pursuing a PhD and their subsequent experiences with the process. Those responsible for overseeing clinical training must be well-informed of the long-term benefits of training academically-qualified physicians. In light of current political uncertainty, universities, hospitals, and external agencies alike must increase their efforts to inspire and assuage early-career clinical academic physicians' fears regarding their academic future.This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

  19. Enabling health care decisionmaking through clinical decision support and knowledge management.

    Science.gov (United States)

    Lobach, David; Sanders, Gillian D; Bright, Tiffani J; Wong, Anthony; Dhurjati, Ravi; Bristow, Erin; Bastian, Lori; Coeytaux, Remy; Samsa, Gregory; Hasselblad, Vic; Williams, John W; Wing, Liz; Musty, Michael; Kendrick, Amy S

    2012-04-01

    To catalogue study designs used to assess the clinical effectiveness of CDSSs and KMSs, to identify features that impact the success of CDSSs/KMSs, to document the impact of CDSSs/KMSs on outcomes, and to identify knowledge types that can be integrated into CDSSs/KMSs. MEDLINE(®), CINAHL(®), PsycINFO(®), and Web of Science(®). We included studies published in English from January 1976 through December 2010. After screening titles and abstracts, full-text versions of articles were reviewed by two independent reviewers. Included articles were abstracted to evidence tables by two reviewers. Meta-analyses were performed for seven domains in which sufficient studies with common outcomes were included. We identified 15,176 articles, from which 323 articles describing 311 unique studies including 160 reports on 148 randomized control trials (RCTs) were selected for inclusion. RCTs comprised 47.5 percent of the comparative studies on CDSSs/KMSs. Both commercially and locally developed CDSSs effectively improved health care process measures related to performing preventive services (n = 25; OR 1.42, 95% confidence interval [CI] 1.27 to 1.58), ordering clinical studies (n = 20; OR 1.72, 95% CI 1.47 to 2.00), and prescribing therapies (n = 46; OR 1.57, 95% CI 1.35 to 1.82). Fourteen CDSS/KMS features were assessed for correlation with success of CDSSs/KMSs across all endpoints. Meta-analyses identified six new success features: Integration with charting or order entry system. Promotion of action rather than inaction. No need for additional clinician data entry. Justification of decision support via research evidence. Local user involvement. Provision of decision support results to patients as well as providers. Three previously identified success features were confirmed: Automatic provision of decision support as part of clinician workflow. Provision of decision support at time and location of decisionmaking. Provision of a recommendation, not just an assessment. Only 29

  20. Analysis of Nursing Clinical Decision Support Requests and Strategic Plan in a Large Academic Health System.

    Science.gov (United States)

    Whalen, Kimberly; Bavuso, Karen; Bouyer-Ferullo, Sharon; Goldsmith, Denise; Fairbanks, Amanda; Gesner, Emily; Lagor, Charles; Collins, Sarah

    2016-01-01

    To understand requests for nursing Clinical Decision Support (CDS) interventions at a large integrated health system undergoing vendor-based EHR implementation. In addition, to establish a process to guide both short-term implementation and long-term strategic goals to meet nursing CDS needs. We conducted an environmental scan to understand current state of nursing CDS over three months. The environmental scan consisted of a literature review and an analysis of CDS requests received from across our health system. We identified existing high priority CDS and paper-based tools used in nursing practice at our health system that guide decision-making. A total of 46 nursing CDS requests were received. Fifty-six percent (n=26) were specific to a clinical specialty; 22 percent (n=10) were focused on facilitating clinical consults in the inpatient setting. "Risk Assessments/Risk Reduction/Promotion of Healthy Habits" (n=23) was the most requested High Priority Category received for nursing CDS. A continuum of types of nursing CDS needs emerged using the Data-Information-Knowledge-Wisdom Conceptual Framework: 1) facilitating data capture, 2) meeting information needs, 3) guiding knowledge-based decision making, and 4) exposing analytics for wisdom-based clinical interpretation by the nurse. Identifying and prioritizing paper-based tools that can be modified into electronic CDS is a challenge. CDS strategy is an evolving process that relies on close collaboration and engagement with clinical sites for short-term implementation and should be incorporated into a long-term strategic plan that can be optimized and achieved overtime. The Data-Information-Knowledge-Wisdom Conceptual Framework in conjunction with the High Priority Categories established may be a useful tool to guide a strategic approach for meeting short-term nursing CDS needs and aligning with the organizational strategic plan.

  1. Virtual clinics in glaucoma care: face-to-face versus remote decision-making.

    Science.gov (United States)

    Clarke, Jonathan; Puertas, Renata; Kotecha, Aachal; Foster, Paul J; Barton, Keith

    2017-07-01

    To examine the agreement in clinical decisions of glaucoma status made in a virtual glaucoma clinic with those made during a face-to-face consultation. A trained nurse and technicians entered data prospectively for 204 patients into a proforma. A subsequent face-to-face clinical assessment was completed by either a glaucoma consultant or fellow. Proformas were reviewed remotely by one of two additional glaucoma consultants, and 12 months later, by the clinicians who had undertaken the original clinical examination. The interobserver and intraobserver decision-making agreements of virtual assessment versus standard care were calculated. We identified adverse disagreement between face-to-face and virtual review in 7/204 (3.4%, 95% CI 0.9% to 5.9%) patients, where virtual review failed to predict a need to accelerated follow-up identified in face-to-face review. Misclassification events were rare, occurring in 1.9% (95% CI 0.3% to 3.8%) of assessments. Interobserver κ (95% CI) showed only fair agreement (0.24 (0.04 to 0.43)); this improved to moderate agreement when only consultant decisions were compared against each other (κ=0.41 (0.16 to 0.65)). The intraobserver agreement κ (95% CI) for the consultant was 0.274 (0.073 to 0.476), and that for the fellow was 0.264 (0.031 to 0.497). The low rate of adverse misclassification, combined with the slowly progressive nature of most glaucoma, and the fact that patients will all be regularly reassessed, suggests that virtual clinics offer a safe, logistically viable option for selected patients with glaucoma. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  2. Reproductive Ethics in Commercial Surrogacy: Decision-Making in IVF Clinics in New Delhi, India.

    Science.gov (United States)

    Tanderup, Malene; Reddy, Sunita; Patel, Tulsi; Nielsen, Birgitte Bruun

    2015-09-01

    As a neo-liberal economy, India has become one of the new health tourism destinations, with commercial gestational surrogacy as an expanding market. Yet the Indian Assisted Reproductive Technology (ART) Bill has been pending for five years, and the guidelines issued by the Indian Council of Medical Research are somewhat vague and contradictory, resulting in self-regulated practices of fertility clinics. This paper broadly looks at clinical ethics in reproduction in the practice of surrogacy and decision-making in various procedures. Through empirical research in New Delhi, the capital of India, from December 2011 to November 2012, issues of decision-making on embryo transfer, fetal reduction, and mode of delivery were identified. Interviews were carried out with doctors in eighteen ART clinics, agents from four agencies, and fourteen surrogates. In aiming to fulfil the commissioning parents' demands, doctors were willing to go to the greatest extent possible in their medical practice. Autonomy and decision-making regarding choice of the number of embryos to transfer and the mode of delivery lay neither with commissioning parents nor surrogate mothers but mostly with doctors. In order to ensure higher success rates, surrogates faced the risk of multiple pregnancy and fetal reduction with little information regarding the risks involved. In the globalized market of commercial surrogacy in India, and with clinics compromising on ethics, there is an urgent need for formulation of regulative law for the clinical practice and maintenance of principles of reproductive ethics in order to ensure that the interests of surrogate mothers are safeguarded.

  3. Automated development of artificial neural networks for clinical purposes: Application for predicting the outcome of choledocholithiasis surgery.

    Science.gov (United States)

    Vukicevic, Arso M; Stojadinovic, Miroslav; Radovic, Milos; Djordjevic, Milena; Cirkovic, Bojana Andjelkovic; Pejovic, Tomislav; Jovicic, Gordana; Filipovic, Nenad

    2016-08-01

    Among various expert systems (ES), Artificial Neural Network (ANN) has shown to be suitable for the diagnosis of concurrent common bile duct stones (CBDS) in patients undergoing elective cholecystectomy. However, their application in practice remains limited since the development of ANNs represents a slow process that requires additional expertize from potential users. The aim of this study was to propose an ES for automated development of ANNs and validate its performances on the problem of prediction of CBDS. Automated development of the ANN was achieved by applying the evolutionary assembling approach, which assumes optimal configuring of the ANN parameters by using Genetic algorithm. Automated selection of optimal features for the ANN training was performed using a Backward sequential feature selection algorithm. The assessment of the developed ANN included the evaluation of predictive ability and clinical utility. For these purposes, we collected data from 303 patients who underwent surgery in the period from 2008 to 2014. The results showed that the total bilirubin, alanine aminotransferase, common bile duct diameter, number of stones, size of the smallest calculus, biliary colic, acute cholecystitis and pancreatitis had the best prognostic value of CBDS. Compared to the alternative approaches, the ANN obtained by the proposed ES had better sensitivity and clinical utility, which are considered to be the most important for the particular problem. Besides the fact that it enabled the development of ANNs with better performances, the proposed ES significantly reduced the complexity of ANNs' development compared to previous studies that required manual selection of optimal features and/or ANN configuration. Therefore, it is concluded that the proposed ES represents a robust and user-friendly framework that, apart from the prediction of CBDS, could advance and simplify the application of ANNs for solving a wider range of problems. Copyright © 2016 Elsevier Ltd

  4. A knowledge- and model-based system for automated weaning from mechanical ventilation: technical description and first clinical application.

    Science.gov (United States)

    Schädler, Dirk; Mersmann, Stefan; Frerichs, Inéz; Elke, Gunnar; Semmel-Griebeler, Thomas; Noll, Oliver; Pulletz, Sven; Zick, Günther; David, Matthias; Heinrichs, Wolfgang; Scholz, Jens; Weiler, Norbert

    2014-10-01

    To describe the principles and the first clinical application of a novel prototype automated weaning system called Evita Weaning System (EWS). EWS allows an automated control of all ventilator settings in pressure controlled and pressure support mode with the aim of decreasing the respiratory load of mechanical ventilation. Respiratory load takes inspired fraction of oxygen, positive end-expiratory pressure, pressure amplitude and spontaneous breathing activity into account. Spontaneous breathing activity is assessed by the number of controlled breaths needed to maintain a predefined respiratory rate. EWS was implemented as a knowledge- and model-based system that autonomously and remotely controlled a mechanical ventilator (Evita 4, Dräger Medical, Lübeck, Germany). In a selected case study (n = 19 patients), ventilator settings chosen by the responsible physician were compared with the settings 10 min after the start of EWS and at the end of the study session. Neither unsafe ventilator settings nor failure of the system occurred. All patients were successfully transferred from controlled ventilation to assisted spontaneous breathing in a mean time of 37 ± 17 min (± SD). Early settings applied by the EWS did not significantly differ from the initial settings, except for the fraction of oxygen in inspired gas. During the later course, EWS significantly modified most of the ventilator settings and reduced the imposed respiratory load. A novel prototype automated weaning system was successfully developed. The first clinical application of EWS revealed that its operation was stable, safe ventilator settings were defined and the respiratory load of mechanical ventilation was decreased.

  5. Interoperability of clinical decision-support systems and electronic health records using archetypes: a case study in clinical trial eligibility.

    Science.gov (United States)

    Marcos, Mar; Maldonado, Jose A; Martínez-Salvador, Begoña; Boscá, Diego; Robles, Montserrat

    2013-08-01

    Clinical decision-support systems (CDSSs) comprise systems as diverse as sophisticated platforms to store and manage clinical data, tools to alert clinicians of problematic situations, or decision-making tools to assist clinicians. Irrespective of the kind of decision-support task CDSSs should be smoothly integrated within the clinical information system, interacting with other components, in particular with the electronic health record (EHR). However, despite decades of developments, most CDSSs lack interoperability features. We deal with the interoperability problem of CDSSs and EHRs by exploiting the dual-model methodology. This methodology distinguishes a reference model and archetypes. A reference model is represented by a stable and small object-oriented model that describes the generic properties of health record information. For their part, archetypes are reusable and domain-specific definitions of clinical concepts in the form of structured and constrained combinations of the entities of the reference model. We rely on archetypes to make the CDSS compatible with EHRs from different institutions. Concretely, we use archetypes for modelling the clinical concepts that the CDSS requires, in conjunction with a series of knowledge-intensive mappings relating the archetypes to the data sources (EHR and/or other archetypes) they depend on. We introduce a comprehensive approach, including a set of tools as well as methodological guidelines, to deal with the interoperability of CDSSs and EHRs based on archetypes. Archetypes are used to build a conceptual layer of the kind of a virtual health record (VHR) over the EHR whose contents need to be integrated and used in the CDSS, associating them with structural and terminology-based semantics. Subsequently, the archetypes are mapped to the EHR by means of an expressive mapping language and specific-purpose tools. We also describe a case study where the tools and methodology have been employed in a CDSS to support

  6. Structures' validation profiles in Transmission of Imaging and Data (TRIAD) for automated National Clinical Trials Network (NCTN) clinical trial digital data quality assurance.

    Science.gov (United States)

    Giaddui, Tawfik; Yu, Jialu; Manfredi, Denise; Linnemann, Nancy; Hunter, Joanne; O'Meara, Elizabeth; Galvin, James; Bialecki, Brian; Xiao, Ying

    2016-01-01

    Transmission of Imaging and Data (TRIAD) is a standard-based system built by the American College of Radiology to provide the seamless exchange of images and data for accreditation of clinical trials and registries. Scripts of structures' names validation profiles created in TRIAD are used in the automated submission process. It is essential for users to understand the logistics of these scripts for successful submission of radiation therapy cases with less iteration. Copyright © 2016 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

  7. The influence of a professional physician network on clinical decision making.

    Science.gov (United States)

    Cohen, Dikla Agur; Levy, Meira; Cohen Castel, Orit; Karkabi, Khaled

    2013-12-01

    The aim of this study was to examine the role of physicians' professional networks in decision-making processes. A professional network was examined in three stages: content analysis and categorization of discussions concerning decision-making processes, in-depth interviews, and a questionnaire. The RAMBAM network has professional as well as social roles. On a professional level, physicians seek approval of their initial line of reasoning regarding their clinical cases, but will consider other approaches if such are suggested by persons of professional repute or if answers are based on evidence-based medicine and include referral to a relevant source. On a social level, physicians want to be part of their professional community and share information and experiences. Physicians' professional networks have a social role that is expressed by a feeling of belonging to a community, as well as a professional role of capturing and disseminating medical knowledge during physicians' decision-making processes. Professional networks constitute a unique source of tacit knowledge that extends existing formal knowledge resources. The study can increase physicians' awareness of professional networks as a unique source of tacit knowledge and can assist in the future design of medical professional networks as knowledge resources for medical decision making. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  8. [Human body meridian spatial decision support system for clinical treatment and teaching of acupuncture and moxibustion].

    Science.gov (United States)

    Wu, Dehua

    2016-01-01

    The spatial position and distribution of human body meridian are expressed limitedly in the decision support system (DSS) of acupuncture and moxibustion at present, which leads to the failure to give the effective quantitative analysis on the spatial range and the difficulty for the decision-maker to provide a realistic spatial decision environment. Focusing on the limit spatial expression in DSS of acupuncture and moxibustion, it was proposed that on the basis of the geographic information system, in association of DSS technology, the design idea was developed on the human body meridian spatial DSS. With the 4-layer service-oriented architecture adopted, the data center integrated development platform was taken as the system development environment. The hierarchical organization was done for the spatial data of human body meridian via the directory tree. The structured query language (SQL) server was used to achieve the unified management of spatial data and attribute data. The technologies of architecture, configuration and plug-in development model were integrated to achieve the data inquiry, buffer analysis and program evaluation of the human body meridian spatial DSS. The research results show that the human body meridian spatial DSS could reflect realistically the spatial characteristics of the spatial position and distribution of human body meridian and met the constantly changeable demand of users. It has the powerful spatial analysis function and assists with the scientific decision in clinical treatment and teaching of acupuncture and moxibustion. It is the new attempt to the informatization research of human body meridian.

  9. General practitioners' decision to refer patients to dietitians: insight into the clinical reasoning process.

    Science.gov (United States)

    Pomeroy, Sylvia E M; Cant, Robyn P

    2010-01-01

    The aim of this project was to describe general practitioners' (GPs') decision-making process for reducing nutrition risk in cardiac patients through referring a patient to a dietitian. The setting was primary care practices in Victoria. The method we employed was mixed methods research: in Study 1, 30 GPs were interviewed. Recorded interviews were transcribed and narratives analysed thematically. Study 2 involved a survey of statewide random sample of GPs. Frequencies and analyses of variance were used to explore the impact of demographic variables on decisions to refer. We found that the referral decision involved four elements: (i) synthesising management information; (ii) forecasting outcomes; (iii) planning management; and (iv) actioning referrals. GPs applied cognitive and collaborative strategies to develop a treatment plan. In Study 2, doctors (248 GPs, 30%) concurred with identified barriers/enabling factors for patients' referral. There was no association between GPs' sex, age or hours worked per week and referral factors. We conclude that a GP's judgment to offer a dietetic referral to an adult patient is a four element reasoning process. Attention to how these elements interact may assist clinical decision making. Apart from the sole use of prescribed medications/surgical procedures for cardiac care, patients offered a dietetic referral were those who were considered able to commit to dietary change and who were willing to attend a dietetic consultation. Improvements in provision of patients' nutrition intervention information to GPs are needed. Further investigation is justified to determine how to resolve this practice gap.

  10. A multiple-scenario assessment of the effect of a continuous-care, guideline-based decision support system on clinicians' compliance to clinical guidelines.

    Science.gov (United States)

    Shalom, Erez; Shahar, Yuval; Parmet, Yisrael; Lunenfeld, Eitan

    2015-04-01

    . Only 5.5% of the decisions were definite errors. In the DSS mode, 94% of the clinicians' decisions were correct, which included 3% that were correct but redundant, and 91% of the actions that were correct and necessary in the context of the GL and of the given scenario. Only 6% of the DSS-mode decisions were erroneous. The DSS was assessed by the clinicians as potentially useful. Support from the GL-based DSS led to uniformity in the quality of the decisions, regardless of the particular clinician, any particular clinical scenario, any particular decision point, or any decision type within the scenarios. Using the DSS dramatically enhances completeness (i.e., performance of guideline-based recommendations) and seems to prevent the performance of most of the redundant actions, but does not seem to affect the rate of performance of incorrect actions. The redundancy rate is enhanced by similar recent findings in recent studies. Clinicians mostly find this support to be potentially useful for their daily practice. A continuous-care GL-based DSS, such as the Picard DSS engine, has the potential to prevent most errors of omission by ensuring uniformly high quality of clinical decision making (relative to a GL-based norm), due to the increased adherence (i.e., completeness) to the GL, and most of the errors of commission that increase therapy costs, by reducing the rate of redundant actions. However, to prevent clinical errors of commission, the DSS needs to be accompanied by additional modules, such as automated control of the quality of the physician's actual actions. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  11. Cervical spine degenerative diseases: An evaluation of clinical and imaging features in surgical decisions

    International Nuclear Information System (INIS)

    Soo, M.; Tran-Dinh, H.D.; Quach, T.; Downey, J.; Pohlmann, S.; Dorsch, N.W.C.

    1997-01-01

    In clinically severe cervical spondylosis, imaging plays a vital role in surgical decisions. A prime factor is acquired canal stenosis with cord compression. To validate this concept, the clinical and imaging features of 20 patients with spondylitic myelopathy and 24 with radiculopathy were retrospectively reviewed. All had computed tomographic myelography (CTM) as part of their clinical work-up. The patients' clinical severity was graded as mild, moderate and severe; the age, length of illness and a history of eventual surgery or otherwise were recorded. At the level of maximum compression the following parameters were obtained from the axial CTM images: surface area and ratio of the anteroposterior to the transverse diameter of the cord; subarachnoid space and vertebral canal areas. Data were statistically analysed. A significant association exists between surgery and increasing severity of symptoms (P=0.04), and advancing age (P=0.01). These associations hold true for myelopathy and radiculopathy. A strong association is present between surgery and the surface area of the cord (P=0.01), being applicable to myelopathy only. The other parameters show no association with surgical decisions. It is concluded that with myelopathy a narrow cord area at the level of maximum compression, and moderate-severe functional impairment are indicators for surgical intervention. (authors)

  12. Cervical spine degenerative diseases: An evaluation of clinical and imaging features in surgical decisions

    Energy Technology Data Exchange (ETDEWEB)

    Soo, M.; Tran-Dinh, H.D.; Quach, T.; Downey, J.; Pohlmann, S. [Westmead Hospital, Westmead, NSW (Australia). Department of Radiology; Dorsch, N.W.C. [Westmead Hospital, Westmead, NSW (Australia). Department of Neurosurgery

    1997-11-01

    In clinically severe cervical spondylosis, imaging plays a vital role in surgical decisions. A prime factor is acquired canal stenosis with cord compression. To validate this concept, the clinical and imaging features of 20 patients with spondylitic myelopathy and 24 with radiculopathy were retrospectively reviewed. All had computed tomographic myelography (CTM) as part of their clinical work-up. The patients` clinical severity was graded as mild, moderate and severe; the age, length of illness and a history of eventual surgery or otherwise were recorded. At the level of maximum compression the following parameters were obtained from the axial CTM images: surface area and ratio of the anteroposterior to the transverse diameter of the cord; subarachnoid space and vertebral canal areas. Data were statistically analysed. A significant association exists between surgery and increasing severity of symptoms (P=0.04), and advancing age (P=0.01). These associations hold true for myelopathy and radiculopathy. A strong association is present between surgery and the surface area of the cord (P=0.01), being applicable to myelopathy only. The other parameters show no association with surgical decisions. It is concluded that with myelopathy a narrow cord area at the level of maximum compression, and moderate-severe functional impairment are indicators for surgical intervention. (authors). 22 refs., 3 tabs., 3 figs.

  13. Technical desiderata for the integration of genomic data with clinical decision support.

    Science.gov (United States)

    Welch, Brandon M; Eilbeck, Karen; Del Fiol, Guilherme; Meyer, Laurence J; Kawamoto, Kensaku

    2014-10-01

    The ease with which whole genome sequence (WGS) information can be obtained is rapidly approaching the point where it can become useful for routine clinical care. However, significant barriers will inhibit widespread adoption unless clinicians are able to effectively integrate this information into patient care and decision-making. Electronic health records (EHR) and clinical decision support (CDS) systems may play a critical role in this integration. A previously published technical desiderata focused primarily on the integration of genomic data into the EHR. This manuscript extends the previous desiderata by specifically addressing needs related to the integration of genomic information with CDS. The objective of this study is to develop and validate a guiding set of technical desiderata for supporting the clinical use of WGS through CDS. A panel of domain experts in genomics and CDS developed a proposed set of seven additional requirements. These desiderata were reviewed by 63 experts in genomics and CDS through an online survey and refined based on the experts' comments. These additional desiderata provide important guiding principles for the technical development of CDS capabilities for the clinical use of WGS information. Copyright © 2014 Elsevier Inc. All rights reserved.

  14. Students' stereotypes of patients as barriers to clinical decision-making.

    Science.gov (United States)

    Johnson, S M; Kurtz, M E; Tomlinson, T; Howe, K R

    1986-09-01

    The ability to formulate quick, accurate clinical judgments is stressed in medical training. Speed is usually an asset when a physician sorts through his biomedical knowledge, but it is often a liability when the physician assesses the sociocultural context of a clinical encounter. At the Michigan State University College of Osteopathic Medicine, a study was designed which graphically illustrated to beginning students that unconscious sociocultural stereotypes may influence clinical decision-making. Three entering classes of students were shown a videotape depicting five simulated patients (attractive black woman, attractive white woman, professional man, middle-aged housewife, and elderly man), each presenting with the same physical complaint. Elements of positive and negative stereotypes were incorporated into each of the portrayals, and the students rated these patients on positive and negative characteristics. The results suggested that the students attributed both positive and negative characteristics to patients on the basis of irrelevant characteristics, such as attractiveness, and with little further justification for their attributions. Such stereotypic generalizations held by students may become barriers to the students' objective clinical decision-making.

  15. Radiographer's impact on improving clinical decision-making, patient care and patient diagnosis: a pilot study

    International Nuclear Information System (INIS)

    Lam, Daniel; Egan, Ingrid; Baird, Marilyn

    2004-01-01

    This pilot study attempts to quantify the benefits of a documented radiographic clinical history through the use of the clinical history template form designed by Egan and Baird. Six radiographers completed the clinical history template for 40 patients and four radiologists included the recorded information as part of their reporting process. A focus discussion group was held between the radiographers to ascertain the level of satisfaction and benefits encountered with the use of the template form. A questionnaire was designed for the radiologists to complete regarding the usefulness of the template form with respect to the radiological reporting process. Results/Discussion: 15 cases for which the form was used demonstrated a direct benefit in respect to improved radiographic clinical decision-making. Radiographers agreed the template form aided the establishment of a stronger radiographer-patient relationship during the radiographic examination. Two radiologists agreed the form aided in establishing a radiological diagnosis and suggested the form be implemented as part of the standard departmental protocol. Despite the small sample size, there is evidence the form aided radiographic decision-making and assisted in the establishment of an accurate radiological diagnosis. The overall consensus amongst radiographers was that it enhanced radiographer-patient communication and improved the level of patient care. Copyright (2004) Australian Institute of Radiography

  16. The integration of surface electromyography in the clinical decision making process: a case report

    Science.gov (United States)

    Nicholson, W Reg

    1998-01-01

    Objective: To demonstrate how the findings of surface electromyography (S.E.M.G.) were integrated into the clinical decision-making process. Clinical Features: This is a retrospective review of the file of a 27-year-old male suffering from mechanical low back pain. He was evaluated on 3 separate occasions over a 3 year period. History, radiography, functional outcome studies, visual-numerical pain score, pain drawing, physical examination and surface electromyography were utilized in evaluating this patient. Intervention and Outcome: The two clinical interventions of spinal manipulative therapy (S.M.T.) had positive results in that the patient achieved an asymptomatic state and returned to his position of employment. The S.E.M.G. data collected during the industrial assessment, did not provide the outcome that the patient had anticipated. Conclusion: Surface electromyography is a useful clinical tool in the author’s decision-making process for the treatment of mechanical lower back pain. Therapeutic intervention by S.M.T., therapeutic exercises and rating risk factors were influenced by the S.E.M.G. findings.

  17. The role of analogy-guided learning experiences in enhancing students' clinical decision-making skills.

    Science.gov (United States)

    Edelen, Bonnie Gilbert; Bell, Alexandra Alice

    2011-08-01

    The purpose of this study was to address the need for effective educational interventions to promote students' clinical decision making (CDM) within clinical practice environments. Researchers used a quasi-experimental, non-equivalent groups, posttest-only design to assess differences in CDM ability between intervention group students who participated in analogy-guided learning activities and control group students who participated in traditional activities. For the intervention, analogy-guided learning activities were incorporated into weekly group discussions, reflective journal writing, and questioning with clinical faculty. The researcher-designed Assessment of Clinical Decision Making Rubric was used to assess indicators of CDM ability in all students' reflective journal entries. Results indicated that the intervention group demonstrated significantly higher levels of CDM ability in their journals compared with the control group (ES(sm) = 0.52). Recommendations provide nurse educators with strategies to maximize students' development of CDM ability, better preparing students for the demands they face when they enter the profession. Copyright 2011, SLACK Incorporated.

  18. Trail Blazing or Jam Session? Towards a New Concept of Clinical Decision-making.

    Science.gov (United States)

    Risør, Torsten

    2017-04-01

    Clinical decision-making (CDM) is key in learning to be a doctor as the defining activity in their clinical work. CDM is often portrayed in the literature as similar to 'trail blazing'; the doctor as the core agent, clearing away obstacles on the path towards diagnosis and treatment. However, in a fieldwork of young doctors in Denmark, it was difficult connect their practice to this image. This paper presents the exploration of this discrepancy in the heart of medical practice and how an alternative image emerged; that of a 'jam session'. The exploration is represented as a case-based hypothesis-testing: first, a theoretically and empirically informed hypothesis (H0) of how doctors perform CDM is developed. In H0, CDM is a stepwise process of reasoning about clinical data, often influenced by outside contextual factors. Then, H0 is tested against a case from ethnographic fieldwork with doctors going through internship. Although the case is chosen for characteristics that make it 'most likely' to verify the hypothesis, verification proves difficult. The case challenges preconceptions in CDM literature about chronology, context, objectivity, cognition, agency, and practice. The young doctor is found not to make decisions, but rather to participate in CDM; an activity akin to the dynamics found in a jam session. Their participation circles in and through four concurrent interrelated constructions that suggest a new conceptualization of CDM; a starting point for a deeper understanding of actual practice in a changing clinical environment.

  19. Reproductive Ethics in Commercial Surrogacy: Decision-Making in IVF Clinics in New Delhi, India

    DEFF Research Database (Denmark)

    Tanderup, Malene; Reddy, Sunita; Patel, Tulsi

    2015-01-01

    As a neo-liberal economy, India has become one of the new health tourism destinations, with Commercial gestational surrogacy as an expanding market. Yet the Indian Assisted Reproductive Technology (ART) Bill has been pending for five years, and the guidelines issued by the Indian Council of Medical...... Research are somewhat vague and contradictory, resulting in self-regulated practices of fertility clinics. This paper broadly looks at clinical ethics in reproduction in the practice of surrogacy and decision-making in various procedures. Through empirical research in New Delhi, the capital of India, from...... success rates, surrogates faced the risk of multiple pregnancy and fetal reduction with little information regarding the risks involved. In the globalized market of Commercial surrogacy in India, and with clinics compromising on ethics, there is an urgent need for formulation of regulative law...

  20. Patient Electronic Health Data–Driven Approach to Clinical Decision Support

    Science.gov (United States)

    Mane, Ketan K.; Bizon, Chris; Owen, Phillips; Gersing, Ken; Mostafa, Javed; Schmitt, Charles

    2011-01-01

    Abstract  This article presents a novel visual analytics (VA)‐based clinical decision support (CDS) tool prototype that was designed as a collaborative work between Renaissance Computing Institute and Duke University. Using Major Depressive Disorder data from MindLinc electronic health record system at Duke, the CDS tool shows an approach to leverage data from comparative population (patients with similar medical profile) to enhance a clinicians’ decision making process at the point of care. The initial work is being extended in collaboration with the University of North Carolina CTSA to address the key challenges of CDS, as well as to show the use of VA to derive insight from large volumes of Electronic Health Record patient data. Clin Trans Sci 2011; Volume 4: 369–371 PMID:22029811

  1. Perforated mucinous cystadenoma of the vermiform appendix: an overview in reasoning clinical decisions.

    Science.gov (United States)

    Papadopoulos, Iordanis N; Christodoulou, Spyridon; Kokoropoulos, Panayiotis; Konstantudakis, George; Economopoulos, Nikolaos; Leontara, Vassilia

    2011-08-29

    Recent advances in the management of appendiceal mucinous neoplasms (AMN) such as peritonectomy combined with hyperthermic intraperitoneal chemotherapy have introduced new standards of care. However, many dilemmas are encountered in decision making as in the following patient. A 74-year-old woman was admitted with an appendiceal cystadenoma found in a preadmission CT scan. However, the tumour was not documented by the in hospital investigation due to its perforation and its reduction in size. Consequently, a series of management dilemmas were encountered that were solved by cautious evaluation of the pre and peroperative findings. She was submitted to a right hemicolectomy. A spontaneous perforation was suspected, but the accurate diagnosis was documented postoperatively by histopathology. This paradigm motivated this review which concluded that reasoning clinical decisions in the light of recent advances and appropriate care based on the disease-stage are essential for an optimal outcome in the management of AMN.

  2. Sharing clinical decisions for multimorbidity case management using social network and open-source tools.

    Science.gov (United States)

    Martínez-García, Alicia; Moreno-Conde, Alberto; Jódar-Sánchez, Francisco; Leal, Sandra; Parra, Carlos

    2013-12-01

    Social networks applied through Web 2.0 tools have gained importance in health domain, because they produce improvements on the communication and coordination capabilities among health professionals. This is highly relevant for multimorbidity patients care because there is a large number of health professionals in charge of patient care, and this requires to obtain clinical consensus in their decisions. Our objective is to develop a tool for collaborative work among health professionals for multimorbidity patient care. We describe the architecture to incorporate decision support functionalities in a social network tool to enable the adoption of shared decisions among health professionals from different care levels. As part of the first stage of the project, this paper describes the results obtained in a pilot study about acceptance and use of the social network component in our healthcare setting. At Virgen del Rocío University Hospital we have designed and developed the Shared Care Platform (SCP) to provide support in the continuity of care for multimorbidity patients. The SCP has two consecutively developed components: social network component, called Clinical Wall, and Clinical Decision Support (CDS) system. The Clinical Wall contains a record where health professionals are able to debate and define shared decisions. We conducted a pilot study to assess the use and acceptance of the SCP by healthcare professionals through questionnaire based on the theory of the Technology Acceptance Model. In March 2012 we released and deployed the SCP, but only with the social network component. The pilot project lasted 6 months in the hospital and 2 primary care centers. From March to September 2012 we created 16 records in the Clinical Wall, all with a high priority. A total of 10 professionals took part in the exchange of messages: 3 internists and 7 general practitioners generated 33 messages. 12 of the 16 record (75%) were answered by the destination health professionals

  3. Clinical reasoning and advanced practice privileges enable physical therapist point-of-care decisions in the military health care system: 3 clinical cases.

    Science.gov (United States)

    Rhon, Daniel I; Deyle, Gail D; Gill, Norman W

    2013-09-01

    Physical therapists frequently make important point-of-care decisions for musculoskeletal injuries and conditions. In the Military Health System (MHS), these decisions may occur while therapists are deployed in support of combat troops, as well as in a more traditional hospital setting. Proficiency with the musculoskeletal examination, including a fundamental understanding of the diagnostic role of musculoskeletal imaging, is an important competency for physical therapists. The purpose of this article is to present 3 cases managed by physical therapists in unique MHS settings, highlighting relevant challenges and clinical decision making. Three cases are presented involving conditions where the physical therapist was significantly involved in the diagnosis and clinical management plan. The physical therapist's clinical privileges, including the ability to order appropriate musculoskeletal imaging procedures, were helpful in making clinical decisions that facilitate timely management. The cases involve patients with an ankle sprain and Maisonneuve fracture, a radial head fracture, and a pelvic neoplasm referred through medical channels as knee pain. Clinical pathways from point of care are discussed, as well as the reasoning that led to decisions affecting definitive care for each of these patients. In each case, emergent treatment and important combat evacuation decisions were based on a combination of examination and management decisions. Physical therapists can provide important contributions to the primary management of patients with musculoskeletal conditions in a variety of settings within the MHS. In the cases described, advanced clinical privileges contributed to the success in this role.

  4. Automated categorization of methicillin-resistant Staphylococcus aureus clinical isolates into different clonal complexes by MALDI-TOF mass spectrometry.

    Science.gov (United States)

    Camoez, M; Sierra, J M; Dominguez, M A; Ferrer-Navarro, M; Vila, J; Roca, I

    2016-02-01

    Early identification of methicillin-resistant Staphylococcus aureus (MRSA) dominant clones involved in infection and initiation of adequate infection control measures are essential to limit MRSA spread and understand MRSA population dynamics. In this study we evaluated the use of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF/MS) for the automated discrimination of the major MRSA lineages (clonal complexes, CC) identified in our hospital during a 20-year period (1990-2009). A collection of 82 well-characterized MRSA isolates belonging to the four main CCs (CC5, CC8, CC22 and CC398) was split into a reference set (n = 36) and a validation set (n = 46) to generate pattern recognition models using the ClinProTools software for the identification of MALDI-TOF/MS biomarker peaks. The supervised neural network (SNN) model showed the best performance compared with two other models, with sensitivity and specificity values of 100% and 99.11%, respectively. Eleven peaks (m/z range: 3278-6592) with the highest separation power were identified and used to differentiate all four CCs. Validation of the SNN model using ClinProTools resulted in a positive predictive value (PPV) of 99.6%. The specific contribution of each peak to the model was used to generate subtyping reference signatures for automated subtyping using the BioTyper software, which successfully classified MRSA isolates into their corresponding CCs with a PPV of 98.9%. In conclusion, we find this novel automated MALDI-TOF/MS approach to be a promising, powerful and reliable tool for S. aureus typing. Copyright © 2015 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

  5. Automated Light- and Dark-Adapted Perimetry for Evaluating Retinitis Pigmentosa: Filling a Need to Accommodate Multicenter Clinical Trials.

    Science.gov (United States)

    McGuigan, David B; Roman, Alejandro J; Cideciyan, Artur V; Matsui, Rodrigo; Gruzensky, Michaela L; Sheplock, Rebecca; Jacobson, Samuel G

    2016-06-01

    The purpose of this study was to develop a convenient means to measure rod (and cone) function by automated perimetry in patients with inherited retinal degenerations (IRDs). A currently available automated perimeter was used to determine sensitivity (in decibels) to a blue target in the dark-adapted (DA) state and a white target in the light-adapted (LA) state. Normal subjects and IRD patients were evaluated with a full-threshold 71-locus strategy (the retinitis pigmentosa [RP] test) and a size III target. Comparisons were made with results from the more commonly used methods of two-color DA perimetry and middle/long-wavelength LA perimetry in the same patients. Rod function using the blue target and the RP test was determined for normal subjects by measuring DA sensitivities. If patients detected the blue stimulus in the DA state, it was determined whether the value was rod mediated by using normal data acquired during the cone plateau phase of dark adaptation. If rod mediated, rod sensitivity loss (RSL) was calculated and mapped across the visual field. Light-adapted sensitivities in normal subjects were also measured, permitting cone sensitivity losses (CSL) to be calculated for the patients. Multiple methods were used to compare RSL and CSL results with those from two-color DA perimetry and chromatic LA perimetry, and there was close correspondence between the methods. The unmodified automated static perimeter used in the DA and LA states presents a practical approach to accomplish current goals of treatment trials in IRDs. This proof-of-principle study is an initial step toward establishing a clinical method to gather reproducible data on photoreceptor-mediated sensitivity.

  6. Dynamic Clinical Algorithms: Digital Technology Can Transform Health Care Decision-Making.

    Science.gov (United States)

    Bell, David; Gachuhi, Noni; Assefi, Nassim

    2018-01-01

    Most health care in low-income countries is delivered at a primary care level by health workers who lack quality training and supervision, often distant from more experienced support. Lack of knowledge and poor communication result in a poor quality of care and inefficient delivery of health services. Although bringing great benefits in sectors such as finance and telecommunication in recent years, the Digital Revolution has lightly and inconsistently affected the health sector. These advances offer an opportunity to dramatically transform health care by increasing the availability and timeliness of information to augment clinical decision-making, based on improved access to patient histories, current information on disease epidemiology, and improved incorporation of data from point-of-care and centralized diagnostic testing. A comprehensive approach is needed to more effectively incorporate current digital technologies into health systems, bringing external and patient-derived data into the clinical decision-making process in real time, irrespective of health worker training or location. Such dynamic clinical algorithms could provide a more effective framework within which to design and integrate new digital health technologies and deliver improved patient care by primary care health workers.

  7. A study of diverse clinical decision support rule authoring environments and requirements for integration

    Directory of Open Access Journals (Sweden)

    Zhou Li

    2012-11-01

    Full Text Available Abstract Background Efficient rule authoring tools are critical to allow clinical Knowledge Engineers (KEs, Software Engineers (SEs, and Subject Matter Experts (SMEs to convert medical knowledge into machine executable clinical decision support rules. The goal of this analysis was to identify the critical success factors and challenges of a fully functioning Rule Authoring Environment (RAE in order to define requirements for a scalable, comprehensive tool to manage enterprise level rules. Methods The authors evaluated RAEs in active use across Partners Healthcare, including enterprise wide, ambulatory only, and system specific tools, with a focus on rule editors for reminder and medication rules. We conducted meetings with users of these RAEs to discuss their general experience and perceived advantages and limitations of these tools. Results While the overall rule authoring process is similar across the 10 separate RAEs, the system capabilities and architecture vary widely. Most current RAEs limit the ability of the clinical decision support (CDS interventions to be standardized, sharable, interoperable, and extensible. No existing system meets all requirements defined by knowledge management users. Conclusions A successful, scalable, integrated rule authoring environment will need to support a number of key requirements and functions in the areas of knowledge representation, metadata, terminology, authoring collaboration, user interface, integration with electronic health record (EHR systems, testing, and reporting.

  8. Development and impact of computerised decision support systems for clinical management of depression: A systematic review.

    Science.gov (United States)

    Triñanes, Yolanda; Atienza, Gerardo; Louro-González, Arturo; de-las-Heras-Liñero, Elena; Alvarez-Ariza, María; Palao, Diego J

    2015-01-01

    One of the proposals for improving clinical practice is to introduce computerised decision support systems (CDSS) and integrate these with electronic medical records. Accordingly, this study sought to systematically review evidence on the effectiveness of CDSS in the management of depression. A search was performed in Medline, EMBASE and PsycInfo, in order to do this. The quality of quantitative studies was assessed using the SIGN method, and qualitative studies using the CASPe checklist. Seven studies were identified (3 randomised clinical trials, 3 non-randomised trials, and one qualitative study). The CDSS assessed incorporated content drawn from guidelines and other evidence-based products. In general, the CDSS had a positive impact on different aspects, such as the screening and diagnosis, treatment, improvement in depressive symptoms and quality of life, and referral of patients. The use of CDSS could thus serve to optimise care of depression in various scenarios by providing recommendations based on the best evidence available and facilitating decision-making in clinical practice. Copyright © 2014 SEP y SEPB. Published by Elsevier España. All rights reserved.

  9. Influence of Cone-beam Computed Tomography in Clinical Decision Making among Specialists.

    Science.gov (United States)

    Rodríguez, Gustavo; Abella, Francesc; Durán-Sindreu, Fernando; Patel, Shanon; Roig, Miguel

    2017-02-01

    Clinical information and diagnostic imaging are essential components of preoperative diagnosis. The aim of this study was to determine the influence of cone-beam computed tomographic (CBCT) imaging on clinical decision-making choices among different specialists (prosthodontists, endodontists, oral surgeons, and periodontists) in endodontic treatment planning. A secondary objective was to assess the self-reported level of difficulty in making a treatment choice before and after viewing a preoperative CBCT scan. In accordance with the endodontic case difficulty guidelines of the American Association of Endodontists, 30 endodontic cases with varying degrees of complexity were selected. Each case included clinical photographs, digital periapical radiographs, and a small-volume CBCT scan. In the first evaluation, examiners were given all the information of each case, except the CBCT scan. Examiners were asked to select one of the proposed treatment alternatives and assess the difficulty of making a decision. One month later, the examiners reviewed randomly the same 30 cases with the additional information from the CBCT data. The CBCT scans only had a significant influence on the treatment plan when the endodontic case was classified as high difficulty (P making among specialists, particularly in high difficulty cases. Copyright © 2016 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

  10. A study of diverse clinical decision support rule authoring environments and requirements for integration.

    Science.gov (United States)

    Zhou, Li; Karipineni, Neelima; Lewis, Janet; Maviglia, Saverio M; Fairbanks, Amanda; Hongsermeier, Tonya; Middleton, Blackford; Rocha, Roberto A

    2012-11-12

    Efficient rule authoring tools are critical to allow clinical Knowledge Engineers (KEs), Software Engineers (SEs), and Subject Matter Experts (SMEs) to convert medical knowledge into machine executable clinical decision support rules. The goal of this analysis was to identify the critical success factors and challenges of a fully functioning Rule Authoring Environment (RAE) in order to define requirements for a scalable, comprehensive tool to manage enterprise level rules. The authors evaluated RAEs in active use across Partners Healthcare, including enterprise wide, ambulatory only, and system specific tools, with a focus on rule editors for reminder and medication rules. We conducted meetings with users of these RAEs to discuss their general experience and perceived advantages and limitations of these tools. While the overall rule authoring process is similar across the 10 separate RAEs, the system capabilities and architecture vary widely. Most current RAEs limit the ability of the clinical decision support (CDS) interventions to be standardized, sharable, interoperable, and extensible. No existing system meets all requirements defined by knowledge management users. A successful, scalable, integrated rule authoring environment will need to support a number of key requirements and functions in the areas of knowledge representation, metadata, terminology, authoring collaboration, user interface, integration with electronic health record (EHR) systems, testing, and reporting.

  11. The Utilization of a Clinical Decision Support System to Manage Adult Type 2 Diabetes: A Correlational Study

    Science.gov (United States)

    Faught, I. Charie

    2012-01-01

    While the Institute of Medicine (2001) has promoted health information technology to improve the process of care such as compliance with clinical practice guidelines and quicker access to clinical information, diagnostic tests, and treatment results, very little was known about how a clinical decision support system can contribute to diabetes…

  12. Exploring a Laboratory Model of Pharmacogenetics as Applied to Clinical Decision Making

    Directory of Open Access Journals (Sweden)

    David F. Kisor

    2013-01-01

    Full Text Available Objective: To evaluate a pilot of a laboratory model for relating pharmacogenetics to clinical decision making. Case Study: This pilot was undertaken and evaluated to help determine if a pharmacogenetics laboratory should be included in the core Doctor of Pharmacy curriculum. The placement of the laboratory exercise in the curriculum was determined by identifying the point in the curriculum where the students had been introduced to the chemistry of deoxyribonucleic acid (DNA as well as instructed on the chemistry of genetic variation. The laboratory included cytochrome P450 2C19 genotyping relative to the *2 variant. Twenty-four students served as the pilot group. Students provided buccal swabs as the source of DNA. Students stabilized the samples and were then provided instructions related to sample preparation, polymerase chain reaction, and gel electrophoresis. The results were reported as images of gels. Students used a reference gel image to compare their results to. Students then applied a dosing algorithm to make a "clinical decision" relative to clopidogrel use. Students were offered a post laboratory survey regarding attitudes toward the laboratory. Twenty-four students completed the laboratory with genotyping results being provided for 22 students (91.7%. Sixteen students were wild-type (*1/*1, while six students were heterozygous (*1/*2. Twenty-three students (96% completed the post laboratory survey. All 23 agreed (6, 26.1% or strongly agreed (17, 73.9% that the laboratory "had relevance and value in the pharmacy curriculum" Conclusion: The post pilot study survey exploring a laboratory model for pharmacogenetics related to clinical decision making indicated that such a laboratory would be viewed positively by students. This model may be adopted by colleges to expand pharmacogenetics education.   Type: Case Study

  13. Clinicians' Use of Prescription Drug Monitoring Programs in Clinical Practice and Decision-Making.

    Science.gov (United States)

    Leichtling, Gillian J; Irvine, Jessica M; Hildebran, Christi; Cohen, Deborah J; Hallvik, Sara E; Deyo, Richard A

    2017-06-01

     Little is known about clinicians' use of prescription drug monitoring program (PDMP) profiles in decision-making. The objective of this qualitative study was to understand how clinicians use, interpret, and integrate PDMP profiles with other information in making clinical decisions.  Qualitative interviews of clinician PDMP users.  Oregon registrants in the state's PDMP.  Thirty-three clinicians practicing in primary care, emergency medicine, pain management, psychiatry, dentistry, and surgery.  We conducted semistructured telephone interviews with PDMP users. A multidisciplinary team used a grounded theory approach to identify patterns of PDMP use and how PDMP profiles influence clinical decisions.  PDMP use varied from consistent monitoring to checking the PDMP only on suspicion of misuse, with inconsistent use reported particularly among short-term prescribers. Primary care clinicians reported less routine use with existing pain patients than with new patients. In response to worrisome PDMP profiles with new patients, participants reported declining to prescribe, except in the case of acute, verifiable conditions. Long-term prescribers reported sometimes continuing prescriptions for existing patients depending on perceived patient intent, honesty, and opioid misuse risk. Some long-term prescribers reported discharging patients from their practices due to worrisome PDMP profiles; others expressed strong ethical grounds for retaining patients but discontinuing controlled substances.  Greater consistency is needed in use of PDMP in monitoring existing patients and in conformity to guidelines against discharging patients from practice. Research is needed to determine optimal approaches to interpreting PDMP profiles in relation to clinical judgment, patient screeners, and other information. © 2016 American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

  14. Translating shared decision-making into health care clinical practices: Proof of concepts

    Directory of Open Access Journals (Sweden)

    St-Jacques Sylvie

    2008-01-01

    Full Text Available Abstract Background There is considerable interest today in shared decision-making (SDM, defined as a decision-making process jointly shared by patients and their health care provider. However, the data show that SDM has not been broadly adopted yet. Consequently, the main goal of this proposal is to bring together the resources and the expertise needed to develop an interdisciplinary and international research team on the implementation of SDM in clinical practice using a theory-based dyadic perspective. Methods Participants include researchers from Canada, US, UK, and Netherlands, representing medicine, nursing, psychology, community health and epidemiology. In order to develop a collaborative research network that takes advantage of the expertise of the team members, the following research activities are planned: 1 establish networking and on-going communication through internet-based forum, conference calls, and a bi-weekly e-bulletin; 2 hold a two-day workshop with two key experts (one in theoretical underpinnings of behavioral change, and a second in dyadic data analysis, and invite all investigators to present their views on the challenges related to the implementation of SDM in clinical practices; 3 conduct a secondary analyses of existing dyadic datasets to ensure that discussion among team members is grounded in empirical data; 4 build capacity with involvement of graduate students in the workshop and online forum; and 5 elaborate a position paper and an international multi-site study protocol. Discussion This study protocol aims to inform researchers, educators, and clinicians interested in improving their understanding of effective strategies to implement shared decision-making in clinical practice using a theory-based dyadic perspective.

  15. Automatic Decision Support for Clinical Diagnostic Literature Using Link Analysis in a Weighted Keyword Network.

    Science.gov (United States)

    Li, Shuqing; Sun, Ying; Soergel, Dagobert

    2017-12-23

    We present a novel approach to recommending articles from the medical literature that support clinical diagnostic decision-making, giving detailed descriptions of the associated ideas and principles. The specific goal is to retrieve biomedical articles that help answer questions of a specified type about a particular case. Based on the filtered keywords, MeSH(Medical Subject Headings) lexicon and the automatically extracted acronyms, the relationship between keywords and articles was built. The paper gives a detailed description of the process of by which keywords were measured and relevant articles identified based on link analysis in a weighted keywords network. Some important challenges identified in this study include the extraction of diagnosis-related keywords and a collection of valid sentences based on the keyword co-occurrence analysis and existing descriptions of symptoms. All data were taken from medical articles provided in the TREC (Text Retrieval Conference) clinical decision support track 2015. Ten standard topics and one demonstration topic were tested. In each case, a maximum of five articles with the highest relevance were returned. The total user satisfaction of 3.98 was 33% higher than average. The results also suggested that the smaller the number of results, the higher the average satisfaction. However, a few shortcomings were also revealed since medical literature recommendation for clinical diagnostic decision support is so complex a topic that it cannot be fully addressed through the semantic information carried solely by keywords in existing descriptions of symptoms. Nevertheless, the fact that these articles are actually relevant will no doubt inspire future research.

  16. A diagnosis-based clinical decision rule for spinal pain part 2: review of the literature

    Directory of Open Access Journals (Sweden)

    Hurwitz Eric L

    2008-08-01

    Full Text Available Abstract Background Spinal pain is a common and often disabling problem. The research on various treatments for spinal pain has, for the most part, suggested that while several interventions have demonstrated mild to moderate short-term benefit, no single treatment has a major impact on either pain or disability. There is great need for more accurate diagnosis in patients with spinal pain. In a previous paper, the theoretical model of a diagnosis-based clinical decision rule was presented. The approach is designed to provide the clinician with a strategy for arriving at a specific working diagnosis from which treatment decisions can be made. It is based on three questions of diagnosis. In the current paper, the literature on the reliability and validity of the assessment procedures that are included in the diagnosis-based clinical decision rule is presented. Methods The databases of Medline, Cinahl, Embase and MANTIS were searched for studies that evaluated the reliability and validity of clinic-based diagnostic procedures for patients with spinal pain that have relevance for questions 2 (which investigates characteristics of the pain source and 3 (which investigates perpetuating factors of the pain experience. In addition, the reference list of identified papers and authors' libraries were searched. Results A total of 1769 articles were retrieved, of which 138 were deemed relevant. Fifty-one studies related to reliability and 76 related to validity. One study evaluated both reliability and validity. Conclusion Regarding some aspects of the DBCDR, there are a number of studies that allow the clinician to have a reasonable degree of confidence in his or her findings. This is particularly true for centralization signs, neurodynamic signs and psychological perpetuating factors. There are other aspects of the DBCDR in which a lesser degree of confidence is warranted, and in which further research is needed.

  17. A Critical Review of the Theoretical Frameworks and the Conceptual Factors in the Adoption of Clinical Decision Support Systems.

    Science.gov (United States)

    Khong, Peck Chui Betty; Holroyd, Eleanor; Wang, Wenru

    2015-12-01

    The clinical decision support system is utilized to translate knowledge into evidence-based practice in clinical settings. Many studies have been conducted to understand users' adoption of the clinical decision support system. A critical review was conducted to understand the theoretical or conceptual frameworks used to inform the studies on the adoption of the clinical decision support system. The review identified 15 theoretical and conceptual frameworks using multiple hybrids of theories and concepts. The Technology Acceptance Model was the most frequently used baseline framework combined with frameworks such as the diffusion of innovation, social theory, longitudinal theory, and so on. The results from these articles yielded multiple concepts influencing the adoption of the clinical decision support system. These concepts can be recategorized into nine major concepts, namely, the information system, person (user or patient), social, organization, perceived benefits, emotions, trustability, relevance (fitness), and professionalism. None of the studies found all the nine concepts. That said, most of them have identified the information system, organization, and person concepts as three of its concepts affecting the use of the clinical decision support system. Within each of the concepts, its subconcepts were noted to be very varied. Yet each of these subconcepts has significantly contributed toward the different facets of the concepts. A pluralistic framework was built using the concepts and subconcepts to provide an overall framework construct for future study on the adoption of the clinical decision support system.

  18. Impact of cardiovascular magnetic resonance on management and clinical decision-making in heart failure patients

    Science.gov (United States)

    2013-01-01

    Background Cardiovascular magnetic resonance (CMR) can provide important diagnostic and prognostic information in patients with heart failure. However, in the current health care environment, use of a new imaging modality like CMR requires evidence for direct additive impact on clinical management. We sought to evaluate the impact of CMR on clinical management and diagnosis in patients with heart failure. Methods We prospectively studied 150 consecutive patients with heart failure and an ejection fraction ≤50% referred for CMR. Definitions for “significant clinical impact” of CMR were pre-defined and collected directly from medical records and/or from patients. Categories of significant clinical impact included: new diagnosis, medication change, hospital admission/discharge, as well as performance or avoidance of invasive procedures (angiography, revascularization, device therapy or biopsy). Results Overall, CMR had a significant clinical impact in 65% of patients. This included an entirely new diagnosis in 30% of cases and a change in management in 52%. CMR results directly led to angiography in 9% and to the performance of percutaneous coronary intervention in 7%. In a multivariable model that included clinical and imaging parameters, presence of late gadolinium enhancement (LGE) was the only independent predictor of “significant clinical impact” (OR 6.72, 95% CI 2.56-17.60, p=0.0001). Conclusions CMR made a significant additive clinical impact on management, decision-making and diagnosis in 65% of heart failure patients. This additive impact was seen despite universal use of prior echocardiography in this patient group. The presence of LGE was the best independent predictor of significant clinical impact following CMR. PMID:24083836

  19. Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation.

    Science.gov (United States)

    Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet

    2017-06-01

    This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care.

  20. Implementing clinical decision support for primary care professionals – the process

    DEFF Research Database (Denmark)

    Kortteisto, Tiina; Komulainen, Jorma; Kunnamo, Ilkka

    2012-01-01

    We describe the process of putting into practice a computer-based clinical decision support (eCDS) service integrated in the electronic patient record, and the actual use of eCDS after one year in a primary care organization with 48 health care professionals. Multiple methods were used to support...... the implementation. The actual use was measured by means of a questionnaire and statistical data. The implementation process consisted of three successive training rounds and lasted for 18 months. After 12 months the reported actual use of the eCDS functions was diverse. The study indicates that successful...

  1. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: Methods of a decision-maker-researcher partnership systematic review

    Directory of Open Access Journals (Sweden)

    Wilczynski Nancy L

    2010-02-01

    Full Text Available Abstract Background Computerized clinical decision support systems are information technology-based systems designed to improve clinical decision-making. As with any healthcare intervention with claims to improve process of care or patient outcomes, decision support systems should be rigorously evaluated before widespread dissemination into clinical practice. Engaging healthcare providers and managers in the review process may facilitate knowledge translation and uptake. The objective of this research was to form a partnership of healthcare providers, managers, and researchers to review randomized controlled trials assessing the effects of computerized decision support for six clinical application areas: primary preventive care, therapeutic drug monitoring and dosing, drug prescribing, chronic disease management, diagnostic test ordering and interpretation, and acute care management; and to identify study characteristics that predict benefit. Methods The review was undertaken by the Health Information Research Unit, McMaster University, in partnership with Hamilton Health Sciences, the Hamilton, Niagara, Haldimand, and Brant Local Health Integration Network, and pertinent healthcare service teams. Following agreement on information needs and interests with decision-makers, our earlier systematic review was updated by searching Medline, EMBASE, EBM Review databases, and Inspec, and reviewing reference lists through 6 January 2010. Data extraction items were expanded according to input from decision-makers. Authors of primary studies were contacted to confirm data and to provide additional information. Eligible trials were organized according to clinical area of application. We included randomized controlled trials that evaluated the effect on practitioner performance or patient outcomes of patient care provided with a computerized clinical decision support system compared with patient care without such a system. Results Data will be summarized

  2. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: methods of a decision-maker-researcher partnership systematic review.

    Science.gov (United States)

    Haynes, R Brian; Wilczynski, Nancy L

    2010-02-05

    Computerized clinical decision support systems are information technology-based systems designed to improve clinical decision-making. As with any healthcare intervention with claims to improve process of care or patient outcomes, decision support systems should be rigorously evaluated before widespread dissemination into clinical practice. Engaging healthcare providers and managers in the review process may facilitate knowledge translation and uptake. The objective of this research was to form a partnership of healthcare providers, managers, and researchers to review randomized controlled trials assessing the effects of computerized decision support for six clinical application areas: primary preventive care, therapeutic drug monitoring and dosing, drug prescribing, chronic disease management, diagnostic test ordering and interpretation, and acute care management; and to identify study characteristics that predict benefit. The review was undertaken by the Health Information Research Unit, McMaster University, in partnership with Hamilton Health Sciences, the Hamilton, Niagara, Haldimand, and Brant Local Health Integration Network, and pertinent healthcare service teams. Following agreement on information needs and interests with decision-makers, our earlier systematic review was updated by searching Medline, EMBASE, EBM Review databases, and Inspec, and reviewing reference lists through 6 January 2010. Data extraction items were expanded according to input from decision-makers. Authors of primary studies were contacted to confirm data and to provide additional information. Eligible trials were organized according to clinical area of application. We included randomized controlled trials that evaluated the effect on practitioner performance or patient outcomes of patient care provided with a computerized clinical decision support system compared with patient care without such a system. Data will be summarized using descriptive summary measures, including proportions

  3. Automated Methods to Extract Patient New Information from Clinical Notes in Electronic Health Record Systems

    Science.gov (United States)

    Zhang, Rui

    2013-01-01

    The widespread adoption of Electronic Health Record (EHR) has resulted in rapid text proliferation within clinical care. Clinicians' use of copying and pasting functions in EHR systems further compounds this by creating a large amount of redundant clinical information in clinical documents. A mixture of redundant information (especially outdated…

  4. A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support.

    Science.gov (United States)

    Connolly, Brian; Cohen, K Bretonnel; Santel, Daniel; Bayram, Ulya; Pestian, John

    2017-08-07

    Probabilistic assessments of clinical care are essential for quality care. Yet, machine learning, which supports this care process has been limited to categorical results. To maximize its usefulness, it is important to find novel approaches that calibrate the ML output with a likelihood scale. Current state-of-the-art calibration methods are generally accurate and applicable to many ML models, but improved granularity and accuracy of such methods would increase the information available for clinical decision making. This novel non-parametric Bayesian approach is demonstrated on a variety of data sets, including simulated classifier outputs, biomedical data sets from the University of California, Irvine (UCI) Machine Learning Repository, and a clinical data set built to determine suicide risk from the language of emergency department patients. The method is first demonstrated on support-vector machine (SVM) models, which generally produce well-behaved, well understood scores. The method produces calibrations that are comparable to the state-of-the-art Bayesian Binning in Quantiles (BBQ) method when the SVM models are able to effectively separate cases and controls. However, as the SVM models' ability to discriminate classes decreases, our approach yields more granular and dynamic calibrated probabilities comparing to the BBQ method. Improvements in granularity and range are even more dramatic when the discrimination between the classes is artificially degraded by replacing the SVM model with an ad hoc k-means classifier. The method allows both clinicians and patients to have a more nuanced view of the output of an ML model, allowing better decision making. The method is demonstrated on simulated data, various biomedical data sets and a clinical data set, to which diverse ML methods are applied. Trivially extending the method to (non-ML) clinical scores is also discussed.

  5. Automatic identification of high impact articles in PubMed to support clinical decision making.

    Science.gov (United States)

    Bian, Jiantao; Morid, Mohammad Amin; Jonnalagadda, Siddhartha; Luo, Gang; Del Fiol, Guilherme

    2017-09-01

    The practice of evidence-based medicine involves integrating the latest best available evidence into patient care decisions. Yet, critical barriers exist for clinicians' retrieval of evidence that is relevant for a particular patient from primary sources such as randomized controlled trials and meta-analyses. To help address those barriers, we investigated machine learning algorithms that find clinical studies with high clinical impact from PubMed®. Our machine learning algorithms use a variety of features including bibliometric features (e.g., citation count), social media attention, journal impact factors, and citation metadata. The algorithms were developed and evaluated with a gold standard composed of 502 high impact clinical studies that are referenced in 11 clinical evidence-based guidelines on the treatment of various diseases. We tested the following hypotheses: (1) our high impact classifier outperforms a state-of-the-art classifier based on citation metadata and citation terms, and PubMed's® relevance sort algorithm; and (2) the performance of our high impact classifier does not decrease significantly after removing proprietary features such as citation count. The mean top 20 precision of our high impact classifier was 34% versus 11% for the state-of-the-art classifier and 4% for PubMed's® relevance sort (p=0.009); and the performance of our high impact classifier did not decrease significantly after removing proprietary features (mean top 20 precision=34% vs. 36%; p=0.085). The high impact classifier, using features such as bibliometrics, social media attention and MEDLINE® metadata, outperformed previous approaches and is a promising alternative to identifying high impact studies for clinical decision support. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. Design of a nursing clinical decision support system applying nursing diagnosis and nursing evaluation model based data mining.

    Science.gov (United States)

    Kim, Hyungyung; Kim, Insook; Chae, Yougmoon

    2006-01-01

    This study a methodological study; to acquire knowledge on the nursing process by steps of knowledge definition, collection, and representation; then, to design a data warehouse and nursing process clinical decision support system.

  7. How do small groups make decisions? : A theoretical framework to inform the implementation and study of clinical competency committees.

    Science.gov (United States)

    Chahine, Saad; Cristancho, Sayra; Padgett, Jessica; Lingard, Lorelei

    2017-06-01

    In the competency-based medical education (CBME) approach, clinical competency committees are responsible for making decisions about trainees' competence. However, we currently lack a theoretical model for group decision-making to inform this emerging assessment phenomenon. This paper proposes an organizing framework to study and guide the decision-making processes of clinical competency committees.This is an explanatory, non-exhaustive review, tailored to identify relevant theoretical and evidence-based papers related to small group decision-making. The search was conducted using Google Scholar, Web of Science, MEDLINE, ERIC, and PsycINFO for relevant literature. Using a thematic analysis, two researchers (SC & JP) met four times between April-June 2016 to consolidate the literature included in this review.Three theoretical orientations towards group decision-making emerged from the review: schema, constructivist, and social influence. Schema orientations focus on how groups use algorithms for decision-making. Constructivist orientations focus on how groups construct their shared understanding. Social influence orientations focus on how individual members influence the group's perspective on a decision. Moderators of decision-making relevant to all orientations include: guidelines, stressors, authority, and leadership.Clinical competency committees are the mechanisms by which groups of clinicians will be in charge of interpreting multiple assessment data points and coming to a shared decision about trainee competence. The way in which these committees make decisions can have huge implications for trainee progression and, ultimately, patient care. Therefore, there is a pressing need to build the science of how such group decision-making works in practice. This synthesis suggests a preliminary organizing framework that can be used in the implementation and study of clinical competency committees.

  8. Clinical Decision-Making in the Treatment of Schizophrenia: Focus on Long-Acting Injectable Antipsychotics

    Directory of Open Access Journals (Sweden)

    Ludovic Samalin

    2016-11-01

    Full Text Available The purpose of this study was to identify clinician characteristics associated with higher prescription rates of long-acting injectable (LAI antipsychotics, as well as the sources that influence medical decision-making regarding the treatment of schizophrenia. We surveyed 202 psychiatrists during six regional French conferences (Bordeaux, Lyon, Marseille, Nice, Paris, and Strasbourg. Data on the characteristics of practice, prescription rates of antipsychotic, and information sources about their clinical decisions were collected. Most psychiatrists used second-generation antipsychotics (SGAs, and preferentially an oral formulation, in the treatment of schizophrenia. LAI SGAs were prescribed to 30.4% of schizophrenic patients. The duration and type of practice did not influence the class or formulation of antipsychotics used. The clinicians following the higher percentage of schizophrenic patients were associated with a higher use of LAI antipsychotics and a lower use of oral SGAs. Personal experience, government regulatory approval, and guidelines for the treatment of schizophrenia were the three main contributing factors guiding clinicians’ decision-making regarding the treatment of schizophrenia. The more clinicians follow schizophrenic patients, the more they use LAI antipsychotics. The development of specialized programs with top specialists should lead to better use of LAI antipsychotics in the treatment of schizophrenia.

  9. Data collection and information presentation for optimal decision making by clinical managers--the Autocontrol Project.

    Science.gov (United States)

    Grant, A M; Richard, Y; Deland, E; Després, N; de Lorenzi, F; Dagenais, A; Buteau, M

    1997-01-01

    The Autocontrol methodology has been developed in order to support the optimisation of decision-making and the use of resources in the context of a clinical unit. The theoretical basis relates to quality assurance and information systems and is influenced by management and cognitive research in the health domain. The methodology uses population rather than individual decision making and because of its dynamic feedback design promises to have rapid and profound effect on practice. Most importantly the health care professional is the principle user of the Autocontrol system. In this methodology we distinguish three types of evidence necessary for practice change: practice based or internal evidence, best evidence derived from the literature or external evidence concerning the practice in question, and process based evidence on how to optimise the process of practice change. The software used by the system is of the executive decision support type which facilitates interrogation of large databases. The Autocontrol system is designed to interrogate the data of the patient medical record however the latter often lacks data on concomitant resource use and this must be supplemented. This paper reviews the Autocontrol methodology and gives examples from current studies.

  10. Studying abroad: Exploring factors influencing nursing students' decisions to apply for clinical placements in international settings.

    Science.gov (United States)

    Kent-Wilkinson, Arlene; Dietrich Leurer, Marie; Luimes, Janet; Ferguson, Linda; Murray, Lee

    2015-08-01

    For over 15 years the College of Nursing at the University of Saskatchewan has facilitated study abroad clinical placements in a number of countries to enhance student learning. Nursing students often find their study abroad experience to be a defining moment in their educational program, and in their personal and professional growth. The main objective of this research was to explore factors influencing nursing students' decisions to study abroad. A descriptive longitudinal design study was conducted using an online survey. The Study Abroad Survey was distributed to all undergraduate and graduate nursing students, in all years of all programs, at all sites of the College of Nursing, University of Saskatchewan in Saskatchewan, Canada. A total of 1058 nursing students registered in the 2013-2014 academic year were surveyed. The data were collected using an online survey administered by Campus Labs™ (2014). Students indicated that their interest in study abroad international experiences was high (84%), with many perceived benefits, but barriers to participation were also high for these students. Financial barriers topped the list (71%), followed by family responsibilities (30%) and job obligations (23%). The research highlights the factors behind student decision making related to international placements, and provides the basis for improvements to the College of Nursing's International Study Abroad Program (ISAP). Previous travel and international service learning, resulting in increased perceived value of a study abroad experience may prove to be the more significant factor influencing decision making, rather than financial barrier. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. Factors that influence parental decisions to participate in clinical research: consenters vs nonconsenters.

    Science.gov (United States)

    Hoberman, Alejandro; Shaikh, Nader; Bhatnagar, Sonika; Haralam, Mary Ann; Kearney, Diana H; Colborn, D Kathleen; Kienholz, Michelle L; Wang, Li; Bunker, Clareann H; Keren, Ron; Carpenter, Myra A; Greenfield, Saul P; Pohl, Hans G; Mathews, Ranjiv; Moxey-Mims, Marva; Chesney, Russell W

    2013-06-01

    A child's health, positive perceptions of the research team and consent process, and altruistic motives play significant roles in the decision-making process for parents who consent for their child to enroll in clinical research. This study identified that nonconsenting parents were better educated, had private insurance, showed lower levels of altruism, and less understanding of study design. To determine the factors associated with parental consent for their child's participation in a randomized, placebo-controlled trial. Cross-sectional survey conducted from July 2008 to May 2011. The survey was an ancillary study to the Randomized Intervention for Children with VesicoUreteral Reflux Study. Seven children's hospitals participating in a randomized trial evaluating management of children with vesicoureteral reflux. Parents asked to provide consent for their child's participation in the randomized trial were invited to complete an anonymous online survey about factors influencing their decision. A total of 120 of the 271 (44%) invited completed the survey; 58 of 125 (46%) who had provided consent and 62 of 144 (43%) who had declined consent completed the survey. A 60-question survey examining child, parent, and study characteristics; parental perception of the study; understanding of the design; external influences; and decision-making process. RESULTS Having graduated from college and private health insurance were associated with a lower likelihood of providing consent. Parents who perceived the trial as having a low degree of risk, resulting in greater benefit to their child and other children, causing little interference with standard care, or exhibiting potential for enhanced care, or who perceived the researcher as professional were significantly more likely to consent to participate. Higher levels of understanding of the randomization process, blinding, and right to withdraw were significantly positively associated with consent to participate. CONCLUSIONS AND

  12. Impact of electronic health record clinical decision support on the management of pediatric obesity.

    Science.gov (United States)

    Shaikh, Ulfat; Berrong, Jeanette; Nettiksimmons, Jasmine; Byrd, Robert S

    2015-01-01

    Clinicians vary significantly in their adherence to clinical guidelines for overweight/obesity. This study assessed the impact of electronic health record-based clinical decision support in improving the diagnosis and management of pediatric obesity. The study team programmed a point-of-care alert linked to a checklist and standardized documentation templates to appear during health maintenance visits for overweight/obese children in an outpatient teaching clinic and compared outcomes through medical record reviews of 574 (287 control and 287 intervention) visits. The results demonstrated a statistically significant increase in the diagnosis of overweight/obesity, scheduling of follow-up appointments, frequency of ordering recommended laboratory investigations, and assessment and counseling for nutrition and physical activity. Although clinical guideline adherence increased significantly, it was far from universal. It is unknown if modest improvements in adherence to clinical guidelines translate to improvements in children's health. However, this intervention was relatively easy to implement and produced measurable improvements in health care delivery. © 2014 by the American College of Medical Quality.

  13. Reasoning, evidence, and clinical decision-making: The great debate moves forward.

    Science.gov (United States)

    Loughlin, Michael; Bluhm, Robyn; Buetow, Stephen; Borgerson, Kirstin; Fuller, Jonathan

    2017-10-01

    When the editorial to the first philosophy thematic edition of this journal was published in 2010, critical questioning of underlying assumptions, regarding such crucial issues as clinical decision making, practical reasoning, and the nature of evidence in health care, was still derided by some prominent contributors to the literature on medical practice. Things have changed dramatically. Far from being derided or dismissed as a distraction from practical concerns, the discussion of such fundamental questions, and their implications for matters of practical import, is currently the preoccupation of some of the most influential and insightful contributors to the on-going evidence-based medicine debate. Discussions focus on practical wisdom, evidence, and value and the relationship between rationality and context. In the debate about clinical practice, we are going to have to be more explicit and rigorous in future in developing and defending our views about what is valuable in human life. © 2017 John Wiley & Sons, Ltd.

  14. Multidisciplinary Modelling of Symptoms and Signs with Archetypes and SNOMED-CT for Clinical Decision Support.

    Science.gov (United States)

    Marco-Ruiz, Luis; Maldonado, J Alberto; Karlsen, Randi; Bellika, Johan G

    2015-01-01

    Clinical Decision Support Systems (CDSS) help to improve health care and reduce costs. However, the lack of knowledge management and modelling hampers their maintenance and reuse. Current EHR standards and terminologies can allow the semantic representation of the data and knowledge of CDSS systems boosting their interoperability, reuse and maintenance. This paper presents the modelling process of respiratory conditions' symptoms and signs by a multidisciplinary team of clinicians and information architects with the help of openEHR, SNOMED and clinical information modelling tools for a CDSS. The information model of the CDSS was defined by means of an archetype and the knowledge model was implemented by means of an SNOMED-CT based ontology.

  15. Usability Testing of a Complex Clinical Decision Support Tool in the Emergency Department: Lessons Learned.

    Science.gov (United States)

    Press, Anne; McCullagh, Lauren; Khan, Sundas; Schachter, Andy; Pardo, Salvatore; McGinn, Thomas

    2015-09-10

    As the electronic health record (EHR) becomes the preferred documentation tool across medical practices, health care organizations are pushing for clinical decision support systems (CDSS) to help bring clinical decision support (CDS) tools to the forefront of patient-physician interactions. A CDSS is integrated into the EHR and allows physicians to easily utilize CDS tools. However, often CDSS are integrated into the EHR without an initial phase of usability testing, resulting in poor adoption rates. Usability testing is important because it evaluates a CDSS by testing it on actual users. This paper outlines the usability phase of a study, which will test the impact of integration of the Wells CDSS for pulmonary embolism (PE) diagnosis into a large urban emergency department, where workflow is often chaotic and high stakes decisions are frequently made. We hypothesize that conducting usability testing prior to integration of the Wells score into an emergency room EHR will result in increased adoption rates by physicians. The objective of the study was to conduct usability testing for the integration of the Wells clinical prediction rule into a tertiary care center's emergency department EHR. We conducted usability testing of a CDS tool in the emergency department EHR. The CDS tool consisted of the Wells rule for PE in the form of a calculator and was triggered off computed tomography (CT) orders or patients' chief complaint. The study was conducted at a tertiary hospital in Queens, New York. There were seven residents that were recruited and participated in two phases of usability testing. The usability testing employed a "think aloud" method and "near-live" clinical simulation, where care providers interacted with standardized patients enacting a clinical scenario. Both phases were audiotaped, video-taped, and had screen-capture software activated for onscreen recordings. Phase I: Data from the "think-aloud" phase of the study showed an overall positive outlook on

  16. Identifying best practices for clinical decision support and knowledge management in the field.

    Science.gov (United States)

    Ash, Joan S; Sittig, Dean F; Dykstra, Richard; Wright, Adam; McMullen, Carmit; Richardson, Joshua; Middleton, Blackford

    2010-01-01

    To investigate best practices for implementing and managing clinical decision support (CDS) in community hospitals and ambulatory settings, we carried out a series of ethnographic studies to gather information from nine diverse organizations. Using the Rapid Assessment Process methodology, we conducted surveys, interviews, and observations over a period of two years in eight different geographic regions of the U.S.A. We first utilized a template organizing method for an expedited analysis of the data, followed by a deeper and more time consuming interpretive approach. We identified five major categories of best practices that require careful consideration while carrying out the planning, implementation, and knowledge management processes related to CDS. As more health care organizations implement clinical systems such as computerized provider order entry with CDS, descriptions of lessons learned by CDS pioneers can provide valuable guidance so that CDS can have optimal impact on health care quality.

  17. Paying for treatments? Influences on negotiating clinical need and decision-making for dental implant treatment.

    Science.gov (United States)

    Exley, Catherine E; Rousseau, Nikki S; Steele, Jimmy; Finch, Tracy; Field, James; Donaldson, Cam; Thomason, J Mark; May, Carl R; Ellis, Janice S

    2009-01-12

    The aim of this study is to examine how clinicians and patients negotiate clinical need and treatment decisions within a context of finite resources. Dental implant treatment is an effective treatment for missing teeth, but is only available via the NHS in some specific clinical circumstances. The majority of people who receive this treatment therefore pay privately, often at substantial cost to themselves. People are used to paying towards dental treatment costs. However, dental implant treatment is much more expensive than existing treatments--such as removable dentures. We know very little about how dentists make decisions about whether to offer such treatments, or what patients consider when deciding whether or not to pay for them. Mixed methods will be employed to provide insight and understanding into how clinical need is determined, and what influences people's decision making processes when deciding whether or not to pursue a dental implant treatment. Phase 1 will use a structured scoping questionnaire with all the General dental practitioners (GDPs) in three Primary Care Trust areas (n = 300) to provide base-line data about existing practice in relation to dental implant treatment, and to provide data to develop a systematic sampling procedure for Phase 2. Phases 2 (GDPs) and 3 (patients) use qualitative focused one to one interviews with a sample of these practitioners (up to 30) and their patients (up to 60) to examine their views and experiences of decision making in relation to dental implant treatment. Purposive sampling for phases 2 and 3 will be carried out to ensure participants represent a range of socio-economic circumstances, and choices made. Most dental implant treatment is conducted in primary care. Very little information was available prior to this study about the quantity and type of treatment carried out privately. It became apparent during phase 2 that ISOD treatment was an unusual treatment in primary care. We thus extended our sample

  18. Paying for treatments? Influences on negotiating clinical need and decision-making for dental implant treatment

    Directory of Open Access Journals (Sweden)

    Thomason J Mark

    2009-01-01

    Full Text Available Abstract Background The aim of this study is to examine how clinicians and patients negotiate clinical need and treatment decisions within a context of finite resources. Dental implant treatment is an effective treatment for missing teeth, but is only available via the NHS in some specific clinical circumstances. The majority of people who receive this treatment therefore pay privately, often at substantial cost to themselves. People are used to paying towards dental treatment costs. However, dental implant treatment is much more expensive than existing treatments – such as removable dentures. We know very little about how dentists make decisions about whether to offer such treatments, or what patients consider when deciding whether or not to pay for them. Methods/Design Mixed methods will be employed to provide insight and understanding into how clinical need is determined, and what influences people's decision making processes when deciding whether or not to pursue a dental implant treatment. Phase 1 will use a structured scoping questionnaire with all the General dental practitioners (GDPs in three Primary Care Trust areas (n = 300 to provide base-line data about existing practice in relation to dental implant treatment, and to provide data to develop a systematic sampling procedure for Phase 2. Phases 2 (GDPs and 3 (patients use qualitative focused one to one interviews with a sample of these practitioners (up to 30 and their patients (up to 60 to examine their views and experiences of decision making in relation to dental implant treatment. Purposive sampling for phases 2 and 3 will be carried out to ensure participants represent a range of socio-economic circumstances, and choices made. Discussion Most dental implant treatment is conducted in primary care. Very little information was available prior to this study about the quantity and type of treatment carried out privately. It became apparent during phase 2 that ISOD treatment was an

  19. Application of a diagnosis-based clinical decision guide in patients with neck pain

    Directory of Open Access Journals (Sweden)

    Murphy Donald R

    2011-08-01

    Full Text Available Abstract Background Neck pain (NP is a common cause of disability. Accurate and efficacious methods of diagnosis and treatment have been elusive. A diagnosis-based clinical decision guide (DBCDG; previously referred to as a diagnosis-based clinical decision rule has been proposed which attempts to provide the clinician with a systematic, evidence-based guide in applying the biopsychosocial model of care. The approach is based on three questions of diagnosis. The purpose of this study is to present the prevalence of findings using the DBCDG in consecutive patients with NP. Methods Demographic, diagnostic and baseline outcome measure data were gathered on a cohort of NP patients examined by one of three examiners trained in the application of the DBCDG. Results Data were gathered on 95 patients. Signs of visceral disease or potentially serious illness were found in 1%. Centralization signs were found in 27%, segmental pain provocation signs were found in 69% and radicular signs were found in 19%. Clinically relevant myofascial signs were found in 22%. Dynamic instability was found in 40%, oculomotor dysfunction in 11.6%, fear beliefs in 31.6%, central pain hypersensitivity in 4%, passive coping in 5% and depression in 2%. Conclusion The DBCDG can be applied in a busy private practice environment. Further studies are needed to investigate clinically relevant means to identify central pain hypersensitivity, oculomotor dysfunction, poor coping and depression, correlations and patterns among the diagnostic components of the DBCDG as well as inter-examiner reliability, validity and efficacy of treatment based on the DBCDG.

  20. Application of a diagnosis-based clinical decision guide in patients with low back pain

    Directory of Open Access Journals (Sweden)

    Murphy Donald R

    2011-10-01

    Full Text Available Abstract Background Low back pain (LBP is common and costly. Development of accurate and efficacious methods of diagnosis and treatment has been identified as a research priority. A diagnosis-based clinical decision guide (DBCDG; previously referred to as a diagnosis-based clinical decision rule has been proposed which attempts to provide the clinician with a systematic, evidence-based means to apply the biopsychosocial model of care. The approach is based on three questions of diagnosis. The purpose of this study is to present the prevalence of findings using the DBCDG in consecutive patients with LBP. Methods Demographic, diagnostic and baseline outcome measure data were gathered on a cohort of LBP patients examined by one of three examiners trained in the application of the DBCDG. Results Data were gathered on 264 patients. Signs of visceral disease or potentially serious illness were found in 2.7%. Centralization signs were found in 41%, lumbar and sacroiliac segmental signs in 23% and 27%, respectively and radicular signs were found in 24%. Clinically relevant myofascial signs were diagnosed in 10%. Dynamic instability was diagnosed in 63%, fear beliefs in 40%, central pain hypersensitivity in 5%, passive coping in 3% and depression in 3%. Conclusion The DBCDG can be applied in a busy private practice environment. Further studies are needed to investigate clinically relevant means to identify central pain hypersensitivity, poor coping and depression, correlations and patterns among the diagnostic components of the DBCDG as well as inter-examiner reliability and efficacy of treatment based on the DBCDG.

  1. Application of a diagnosis-based clinical decision guide in patients with neck pain

    Science.gov (United States)

    2011-01-01

    Background Neck pain (NP) is a common cause of disability. Accurate and efficacious methods of diagnosis and treatment have been elusive. A diagnosis-based clinical decision guide (DBCDG; previously referred to as a diagnosis-based clinical decision rule) has been proposed which attempts to provide the clinician with a systematic, evidence-based guide in applying the biopsychosocial model of care. The approach is based on three questions of diagnosis. The purpose of this study is to present the prevalence of findings using the DBCDG in consecutive patients with NP. Methods Demographic, diagnostic and baseline outcome measure data were gathered on a cohort of NP patients examined by one of three examiners trained in the application of the DBCDG. Results Data were gathered on 95 patients. Signs of visceral disease or potentially serious illness were found in 1%. Centralization signs were found in 27%, segmental pain provocation signs were found in 69% and radicular signs were found in 19%. Clinically relevant myofascial signs were found in 22%. Dynamic instability was found in 40%, oculomotor dysfunction in 11.6%, fear beliefs in 31.6%, central pain hypersensitivity in 4%, passive coping in 5% and depression in 2%. Conclusion The DBCDG can be applied in a busy private practice environment. Further studies are needed to investigate clinically relevant means to identify central pain hypersensitivity, oculomotor dysfunction, poor coping and depression, correlations and patterns among the diagnostic components of the DBCDG as well as inter-examiner reliability, validity and efficacy of treatment based on the DBCDG. PMID:21871119

  2. Automated production of [¹⁸F]VAT suitable for clinical PET study of vesicular acetylcholine transporter.

    Science.gov (United States)

    Yue, Xuyi; Bognar, Christopher; Zhang, Xiang; Gaehle, Gregory G; Moerlein, Stephen M; Perlmutter, Joel S; Tu, Zhude

    2016-01-01

    Automated production of a promising radiopharmaceutical (-)-(1-(8-(2-[(18)F]fluoroethoxy)-3-hydroxy-1,2,3,4-tetrahydronaphthalen-2-yl)-piperidin-4-yl)(4-fluorophenyl)methanone ([(18)F]VAT) for the vesicular acetylcholine transporter(VAChT) was achieved using a two-step procedure in a current Good Manufacturing Practices fashion. The production of [(18)F]VAT was accomplished in approximately 140 min, with radiochemical yield of ~15.0% (decay corrected), specific activity>111 GBq/µmol, radiochemical purity>99% and mass of VAT ~3.4 μg/batch (n>10). The radiopharmaceutical product meets all quality control criteria for human use, and is suitable for clinical PET studies of VAChT. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Evaluation of Clinical Decision Rules for Bone Mineral Density Testing among White Women

    Directory of Open Access Journals (Sweden)

    Michael E. Anders

    2013-01-01

    Full Text Available Background. Osteoporosis is a devastating, insidious disease that causes skeletal fragility. Half of women will suffer osteoporotic fractures during their lifetimes. Many fractures occur needlessly, because of inattentiveness to assessment, diagnosis, prevention, and treatment of osteoporosis. Study Purpose. Study Purpose. To evaluate the discriminatory performance of clinical decision rules to determine the need to undergo bone mineral density testing. Methods. A nationally representative sample from the Third National Health and Nutrition Examination Survey consisted of 14,060 subjects who completed surveys, physical examinations, laboratory tests, and bone mineral density exams. Multivariable linear regression tested the correlation of covariates that composed the clinical decision rules with bone mineral density. Results. Increased age and decreased weight were variables in the final regression models for each gender and race/ethnicity. Among the indices, the Osteoporosis Self-Assessment Tool, which is composed of age and weight, performed best for White women. Study Implications. These results have implications for the prevention, assessment, diagnosis, and treatment of osteoporosis. The Osteoporosis Self-Assessment Tool performed best and is inexpensive and the least time consuming to implement.

  4. Are clinical decisions in endodontics influenced by the patient's fee-paying status?

    Science.gov (United States)

    Walker, I; Gilbert, D; Asimakopoulou, K

    2015-12-01

    We explored whether the fee status of a UK patient influences clinical decision-making in endodontics. In a randomised-controlled vignette study describing either an 'NHS-funded', 'Privately-funded' or undisclosed fee-status patient, we examined the importance vocational trainer dentists placed on a series of factors normally considered when deciding whether to offer patients endodontic treatment as opposed to extracting the tooth. N = 119 experienced (M years post qualification = 20.01) dentists participated. Having read a vignette describing a hypothetical patient who could potentially be treated either endodontically or through an extraction, dentists rated a series of factors they would normally consider (for example, poor oral hygiene, the rest of their mouth is unfilled and caries-free), before recommending either endodontic treatment or an extraction. The patient's funding status had no influence on these dentists' clinical decision-making when considering endodontic treatment as an option (p >0.05) with the exception of a single item relating to infrequent attendance where the NHS patient was more likely than the 'undisclosed-fee' patient, to be offered extractions (F (2, 116) 3.43, p endodontic treatment by experienced dentists.

  5. A point-of-care chemistry test for reduction of turnaround and clinical decision time.

    Science.gov (United States)

    Lee, Eui Jung; Shin, Sang Do; Song, Kyoung Jun; Kim, Seong Chun; Cho, Jin Seong; Lee, Seung Chul; Park, Ju Ok; Cha, Won Chul

    2011-06-01

    Our study compared clinical decision time between patients managed with a point-of-care chemistry test (POCT) and patients managed with the traditional central laboratory test (CLT). This was a randomized controlled multicenter trial in the emergency departments (EDs) of 5 academic teaching hospitals. We randomly assigned patients to POCT or CLT stratified by the Emergency Severity Index. A POCT chemistry analyzer (Piccolo; Abaxis, Inc, Union City, Calif), which is able to test liver panel, renal panel, pancreas enzymes, lipid panel, electrolytes, and blood gases, was set up in each ED. Primary and secondary end point was turnaround time and door-to-clinical-decision time. The total 2323 patients were randomly assigned to the POCT group (n = 1167) or to the CLT group (n = 1156). All of the basic characteristics were similar in the 2 groups. The turnaround time (median, interquartile range [IQR]) of the POCT group was shorter than that of the CLT group (14, 12-19 versus 55, 45-69 minutes; P CLT group (46, 33-61 versus 86, 68-107 minutes; P CLT group (P CLT. Copyright © 2011 Elsevier Inc. All rights reserved.

  6. Automated extraction of clinical traits of multiple sclerosis in electronic medical records

    Science.gov (United States)

    Davis, Mary F; Sriram, Subramaniam; Bush, William S; Denny, Joshua C; Haines, Jonathan L

    2013-01-01

    Objectives The clinical course of multiple sclerosis (MS) is highly variable, and research data collection is costly and time consuming. We evaluated natural language processing techniques applied to electronic medical records (EMR) to identify MS patients and the key clinical traits of their disease course. Materials and methods We used four algorithms based on ICD-9 codes, text keywords, and medications to identify individuals with MS from a de-identified, research version of the EMR at Vanderbilt University. Using a training dataset of the records of 899 individuals, algorithms were constructed to identify and extract detailed information regarding the clinical course of MS from the text of the medical records, including clinical subtype, presence of oligoclonal bands, year of diagnosis, year and origin of first symptom, Expanded Disability Status Scale (EDSS) scores, timed 25-foot walk scores, and MS medications. Algorithms were evaluated on a test set validated by two independent reviewers. Results We identified 5789 individuals with MS. For all clinical traits extracted, precision was at least 87% and specificity was greater than 80%. Recall values for clinical subtype, EDSS scores, and timed 25-foot walk scores were greater than 80%. Discussion and conclusion This collection of clinical data represents one of the largest databases of detailed, clinical traits available for research on MS. This work demonstrates that detailed clinical information is recorded in the EMR and can be extracted for research purposes with high reliability. PMID:24148554

  7. Knowledge of Fecal Calprotectin and Infliximab Trough Levels Alters Clinical Decision-making for IBD Outpatients on Maintenance Infliximab Therapy

    Science.gov (United States)

    Prosser, Connie; Kroeker, Karen I.; Wang, Haili; Shalapay, Carol; Dhami, Neil; Fedorak, Darryl K.; Halloran, Brendan; Dieleman, Levinus A.; Goodman, Karen J.; Fedorak, Richard N.

    2015-01-01

    Background: Infliximab is an effective therapy for inflammatory bowel disease (IBD). However, more than 50% of patients lose response. Empiric dose intensification is not effective for all patients because not all patients have objective disease activity or subtherapeutic drug level. The aim was to determine how an objective marker of disease activity or therapeutic drug monitoring affects clinical decisions regarding maintenance infliximab therapy in outpatients with IBD. Methods: Consecutive patients with IBD on maintenance infliximab therapy were invited to participate by providing preinfusion stool and blood samples. Fecal calprotectin (FCP) and infliximab trough levels (ITLs) were measured by enzyme linked immunosorbent assay. Three decisions were compared: (1) actual clinical decision, (2) algorithmic FCP or ITL decisions, and (3) expert panel decision based on (a) clinical data, (b) clinical data plus FCP, and (c) clinical data plus FCP plus ITL. In secondary analysis, Receiver-operating curves were used to assess the ability of FCP and ITL in predicting clinical disease activity or remission. Results: A total of 36 sets of blood and stool were available for analysis; median FCP 191.5 μg/g, median ITLs 7.3 μg/mL. The actual clinical decision differed from the hypothetical decision in 47.2% (FCP algorithm); 69.4% (ITL algorithm); 25.0% (expert panel clinical decision); 44.4% (expert panel clinical plus FCP); 58.3% (expert panel clinical plus FCP plus ITL) cases. FCP predicted clinical relapse (area under the curve [AUC] = 0.417; 95% confidence interval [CI], 0.197–0.641) and subtherapeutic ITL (AUC = 0.774; 95% CI, 0.536–1.000). ITL predicted clinical remission (AUC = 0.498; 95% CI, 0.254–0.742) and objective remission (AUC = 0.773; 95% CI, 0.622–0.924). Conclusions: Using FCP and ITLs in addition to clinical data results in an increased number of decisions to optimize management in outpatients with IBD on stable maintenance infliximab therapy. PMID

  8. New clinical validation method for automated sphygmomanometer: a proposal by Japan ISO-WG for sphygmomanometer standard.

    Science.gov (United States)

    Shirasaki, Osamu; Asou, Yosuke; Takahashi, Yukio

    2007-12-01

    Owing to fast or stepwise cuff deflation, or measuring at places other than the upper arm, the clinical accuracy of most recent automated sphygmomanometers (auto-BPMs) cannot be validated by one-arm simultaneous comparison, which would be the only accurate validation method based on auscultation. Two main alternative methods are provided by current standards, that is, two-arm simultaneous comparison (method 1) and one-arm sequential comparison (method 2); however, the accuracy of these validation methods might not be sufficient to compensate for the suspicious accuracy in lateral blood pressure (BP) differences (LD) and/or BP variations (BPV) between the device and reference readings. Thus, the Japan ISO-WG for sphygmomanometer standards has been studying a new method that might improve validation accuracy (method 3). The purpose of this study is to determine the appropriateness of method 3 by comparing immunity to LD and BPV with those of the current validation methods (methods 1 and 2). The validation accuracy of the above three methods was assessed in human participants [N=120, 45+/-15.3 years (mean+/-SD)]. An oscillometric automated monitor, Omron HEM-762, was used as the tested device. When compared with the others, methods 1 and 3 showed a smaller intra-individual standard deviation of device error (SD1), suggesting their higher reproducibility of validation. The SD1 by method 2 (P=0.004) significantly correlated with the participant's BP, supporting our hypothesis that the increased SD of device error by method 2 is at least partially caused by essential BPV. Method 3 showed a significantly (P=0.0044) smaller interparticipant SD of device error (SD2), suggesting its higher interparticipant consistency of validation. Among the methods of validation of the clinical accuracy of auto-BPMs, method 3, which showed the highest reproducibility and highest interparticipant consistency, can be proposed as being the most appropriate.

  9. Anti-cyclic citrullinated peptide autoantibodies measured by an automated enzyme immunoassay: analytical performance and clinical correlations.

    Science.gov (United States)

    Tampoia, Marilina; Brescia, Vincenzo; Fontana, Antonietta; Maggiolini, Piera; Lapadula, Giovanni; Pansini, Nicola

    2005-05-01

    Autoantibodies against cyclic citrullinated peptide (anti-CCP) are considered to be a sensitive and specific marker for rheumatoid arthritis (RA). This study evaluated the analytical performance and clinical correlation of an automated enzyme immunoassay (DSX, DINEX Technologies), for the detection of anti-CCP autoantibodies (DIASTAT anti-CCP, Axis-Shield, DUNDEE UK). Commercial controls and serum pools were used to determine its precision, analytical sensitivity, functional sensitivity and linearity. Sera from 83 patients with established RA and from 140 controls, including patients with various autoimmune diseases, viral infections and cancer, as well as sex- and age-matched healthy subjects, were studied. The rheumatoid factor (RF) was also assayed in each sample, and the results were compared to the anti-CCP findings. The total imprecision (CV%) was 4.7-7.2% for concentrations ranging between 1.98 and 71.81 U/mL. The lower detection limit was 0.038 U/mL. At a cut-off of 5 U/mL, the sensitivity and specificity for RA were 67.5% and 99.3%, respectively. The RF had a sensitivity of 66.3% and a lower specificity 82.1% than anti-CCP. When the two antibodies were used together, the specificity was 99.1%. The anti-CCP assay we examined on a fully automated system showed a good analytical performance (analytical and functional sensitivity, linearity) and good clinical correlation. We conclude that this system can provide rapid, useful data.

  10. Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention.

    Science.gov (United States)

    Lobach, David F; Johns, Ellis B; Halpenny, Barbara; Saunders, Toni-Ann; Brzozowski, Jane; Del Fiol, Guilherme; Berry, Donna L; Braun, Ilana M; Finn, Kathleen; Wolfe, Joanne; Abrahm, Janet L; Cooley, Mary E

    2016-11-08

    Management of uncontrolled symptoms is an important component of quality cancer care. Clinical guidelines are available for optimal symptom management, but are not often integrated into the front lines of care. The use of clinical decision support (CDS) at the point-of-care is an innovative way to incorporate guideline-based symptom management into routine cancer care. The objective of this study was to develop and evaluate a rule-based CDS system to enable management of multiple symptoms in lung cancer patients at the point-of-care. This study was conducted in three phases involving a formative evaluation, a system evaluation, and a contextual evaluation of clinical use. In Phase 1, we conducted iterative usability testing of user interface prototypes with patients and health care providers (HCPs) in two thoracic oncology clinics. In Phase 2, we programmed complex algorithms derived from clinical practice guidelines into a rules engine that used Web services to communicate with the end-user application. Unit testing of algorithms was conducted using a stack-traversal tree-spanning methodology to identify all possible permutations of pathways through each algorithm, to validate accuracy. In Phase 3, we evaluated clinical use of the system among patients and HCPs in the two clinics via observations, structured interviews, and questionnaires. In Phase 1, 13 patients and 5 HCPs engaged in two rounds of formative testing, and suggested improvements leading to revisions until overall usability scores met a priori benchmarks. In Phase 2, symptom management algorithms contained between 29 and 1425 decision nodes, resulting in 19 to 3194 unique pathways per algorithm. Unit testing required 240 person-hours, and integration testing required 40 person-hours. In Phase 3, both patients and HCPs found the system usable and acceptable, and offered suggestions for improvements. A rule-based CDS system for complex symptom management was systematically developed and tested. The

  11. Integrating usability testing and think-aloud protocol analysis with "near-live" clinical simulations in evaluating clinical decision support.

    Science.gov (United States)

    Li, Alice C; Kannry, Joseph L; Kushniruk, Andre; Chrimes, Dillon; McGinn, Thomas G; Edonyabo, Daniel; Mann, Devin M

    2012-11-01

    Usability evaluations can improve the usability and workflow integration of clinical decision support (CDS). Traditional usability testing using scripted scenarios with think-aloud protocol analysis provide a useful but incomplete assessment of how new CDS tools interact with users and clinical workflow. "Near-live" clinical simulations are a newer usability evaluation tool that more closely mimics clinical workflow and that allows for a complementary evaluation of CDS usability as well as impact on workflow. This study employed two phases of testing a new CDS tool that embedded clinical prediction rules (an evidence-based medicine tool) into primary care workflow within a commercial electronic health record. Phase I applied usability testing involving "think-aloud" protocol analysis of 8 primary care providers encountering several scripted clinical scenarios. Phase II used "near-live" clinical simulations of 8 providers interacting with video clips of standardized trained patient actors enacting the clinical scenario. In both phases, all sessions were audiotaped and had screen-capture software activated for onscreen recordings. Transcripts were coded using qualitative analysis methods. In Phase I, the impact of the CDS on navigation and workflow were associated with the largest volume of negative comments (accounting for over 90% of user raised issues) while the overall usability and the content of the CDS were associated with the most positive comments. However, usability had a positive-to-negative comment ratio of only 0.93 reflecting mixed perceptions about the usability of the CDS. In Phase II, the duration of encounters with simulated patients was approximately 12 min with 71% of the clinical prediction rules being activated after half of the visit had already elapsed. Upon activation, providers accepted the CDS tool pathway 82% of times offered and completed all of its elements in 53% of all simulation cases. Only 12.2% of encounter time was spent using the

  12. Decision Making in the PICU: An Examination of Factors Influencing Participation Decisions in Phase III Randomized Clinical Trials

    Directory of Open Access Journals (Sweden)

    Laura E. Slosky

    2014-01-01

    participate were not related to enrollment. Conclusion. Decisions to participate in research by surrogates of children in the PICU appear to relate to child demographics and subtleties in communication; however, no modifiable characteristics were related to increased participation, indicating that the informed consent process may not be compromised in this population.

  13. The effect of attitude to risk on decisions made by nurses using computerised decision support software in telephone clinical assessment: an observational study.

    Science.gov (United States)

    O'Cathain, Alicia; Munro, James; Armstrong, Iain; O'Donnell, Catherine; Heaney, David

    2007-11-29

    There is variation in the decisions made by telephone assessment nurses using computerised decision support software (CDSS). Variation in nurses' attitudes to risk has been identified as a possible explanatory factor. This study was undertaken to explore the effect of nurses' attitudes to risk on the decisions they make when using CDSS. The setting was NHS 24 which is a nationwide telephone assessment service in Scotland in which nurses assess health problems, mainly on behalf of out-of-hours general practice, and triage calls to self care, a service at a later date, or immediate contact with a service. All NHS 24 nurses were asked to complete a questionnaire about their background and attitudes to risk. Routine data on the decisions made by these nurses was obtained for a six month period in 2005. Multilevel modelling was used to measure the effect of nurses' risk attitudes on the proportion of calls they sent to self care rather than to services. The response rate to the questionnaire was 57% (265/464). 231,112 calls were matched to 211 of these nurses. 16% (36,342/231,112) of calls were sent to self care, varying three fold between the top and bottom deciles of nurses. Fifteen risk attitude variables were tested, including items on attitudes to risk in clinical decision-making. Attitudes to risk varied greatly between nurses, for example 27% (71/262) of nurses strongly agreed that an NHS 24 nurse "must not take any risks with physical illness" while 17% (45/262) disagreed. After case-mix adjustment, there was some evidence that nurses' attitudes to risk affected decisions but this was inconsistent and unconvincing. Much of the variation in decision-making by nurses using CDSS remained unexplained. There was no convincing evidence that nurses' attitudes to risk affected the decisions made. This may have been due to the limitations of the instrument used to measure risk attitude.

  14. Decisions, Decisions!

    Science.gov (United States)

    McFadden, F. Lee

    1975-01-01

    A self-instructional program on decision making was used in conjunction with workshops to introduce the staff of an instructional materials company to the decision tree process as they used it to study their own film production problem. (Author/MS)

  15. Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision.

    Science.gov (United States)

    Middleton, B; Sittig, D F; Wright, A

    2016-08-02

    The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040. Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS. In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery. CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness.

  16. Media portrayal of herbal remedies versus pharmaceutical clinical trials: impacts on decision.

    Science.gov (United States)

    Bubela, T; Koper, M; Boon, H; Caulfield, T

    2007-06-01

    The use of Complementary and Alternative Medicines (CAM) in Europe and North America is increasing significantly with a concomitant growth in business interest. Users are educated and self-empowered and rely on information sources beyond mainstream medical practitioners. Not surprisingly, media coverage, much of dubious quality, has increased to meet demand for information. Here we present data from a study that explores how knowledge is translated in the socioeconomic-political context of CAM as compared to conventional pharmaceuticals. Specifically, we are interested in the nature of the information provided by clinical trials and the media and how this might impact decision-making regarding the use of CAM versus conventional pharmaceuticals and the reporting of conflicts of interest and industry funding of research. Our results suggest that, in the media, there were significant errors of omission in describing clinical trial quality and a serious under-reporting of risks of herbal remedies. Consumers, who often self-administer CAM are not being provided with information sufficient to make informed choices about treatment alternatives. The next step in the research is to determine whether these reporting dynamics in describing CAM clinical trials differ from those of reporting on pharmaceutical clinical trials.

  17. Preliminary Clinical Experience with a Combined Automated Breast Ultrasound and Digital Breast Tomosynthesis System.

    Science.gov (United States)

    Larson, Eric D; Lee, Won-Mean; Roubidoux, Marilyn A; Goodsitt, Mitchell M; Lashbrook, Chris; Davis, Cynthia E; Kripfgans, Oliver D; Carson, Paul L

    2018-03-01

    We analyzed the performance of a mammographically configured, automated breast ultrasound (McABUS) scanner combined with a digital breast tomosynthesis (DBT) system. The GE Invenia ultrasound system was modified for integration with GE DBT systems. Ultrasound and DBT imaging were performed in the same mammographic compression. Our small preliminary study included 13 cases, six of whom had contained invasive cancers. From analysis of these cases, current limitations and corresponding potential improvements of the system were determined. A registration analysis was performed to compare the ease of McABUS to DBT registration for this system with that of two systems designed previously. It was observed that in comparison to data from an earlier study, the McABUS-to-DBT registration alignment errors for both this system and a previously built combined system were smaller than those for a previously built standalone McABUS system. Copyright © 2018 World Federation for Ultrasound in Medicine and Biology. Published by Elsevier Inc. All rights reserved.

  18. Clinical application of automated Greulich-Pyle bone age determination in children with short stature

    Energy Technology Data Exchange (ETDEWEB)

    Martin, David D.; Deusch, Dorothee; Schweizer, Roland; Binder, Gerhard; Ranke, Michael B. [University Children' s Hospital, Paediatric Endocrinology Section, Tuebingen (Germany); Thodberg, Hans Henrik [Visiana, Holte (Denmark)

    2009-06-15

    Bone age (BA) rating is time consuming and highly rater dependent. To adjust the fully automated BoneXpert method to agree with the manual Greulich and Pyle BA (GP BA) ratings of five raters and to validate the accuracy for short children. A total of 1,097 left hand radiographs from 188 children with short stature, including growth hormone deficiency (44%) and Turner syndrome (29%) were evaluated. BoneXpert rejected 14 of the 1,097 radiographs, and deviated by more than 1.9 years from the operator BA for 27 radiographs. These were rerated blindly by four operators. Of the 27 new ratings, 26 were within 1.9 years of the automatic BA values. The root mean square deviation between manual and automatic rating was 0.72 years (95% CI 0.69-0.75). BoneXpert's ability to process 99% of images automatically without errors, and to obtain good agreement with an operator suggests that the method is efficient and reliable for short children. (orig.)

  19. An Automation Survival Guide for Media Centers.

    Science.gov (United States)

    Whaley, Roger E.

    1989-01-01

    Reviews factors that should affect the decision to automate a school media center and offers suggestions for the automation process. Topics discussed include getting the library collection ready for automation, deciding what automated functions are needed, evaluating software vendors, selecting software, and budgeting. (CLB)

  20. mHealth for Clinical Decision-Making in Sub-Saharan Africa: A Scoping Review.

    Science.gov (United States)

    Adepoju, Ibukun-Oluwa Omolade; Albersen, Bregje Joanna Antonia; De Brouwere, Vincent; van Roosmalen, Jos; Zweekhorst, Marjolein

    2017-03-23

    In a bid to deliver quality health services in resource-poor settings, mobile health (mHealth) is increasingly being adopted. The role of mHealth in facilitating evidence-based clinical decision-making through data collection, decision algorithms, and evidence-based guidelines, for example, is established in resource-rich settings. However, the extent to which mobile clinical decision support systems (mCDSS) have been adopted specifically in resource-poor settings such as Africa and the lessons learned about their use in such settings are yet to be established. The aim of this study was to synthesize evidence on the use of mHealth for point-of-care decision support and improved quality of care by health care workers in Africa. A scoping review of 4 peer-reviewed and 1 grey literature databases was conducted. No date limits were applied, but only articles in English language were selected. Using pre-established criteria, 2 reviewers screened articles and extracted data. Articles were analyzed using Microsoft Excel and MAXQDA. We retained 22 articles representing 11 different studies in 7 sub-Saharan African countries. Interventions were mainly in the domain of maternal health and ranged from simple text messaging (short message service, SMS) to complex multicomponent interventions. Although health workers are generally supportive of mCDSS and perceive them as useful, concerns about increased workload and altered workflow hinder sustainability. Facilitators and barriers to use of mCDSS include technical and infrastructural support, ownership, health system challenges, and training. The use of mCDSS in sub-Saharan Africa is an indication of progress in mHealth, although their effect on quality of service delivery is yet to be fully explored. Lessons learned are useful for informing future research, policy, and practice for technologically supported health care delivery, especially in resource-poor settings. ©Ibukun-Oluwa Omolade Adepoju, Bregje Joanna Antonia

  1. Referring periodontal patients: clinical decision making by dental and dental hygiene students.

    Science.gov (United States)

    Williams, Karen B; Burgardt, Grayson J; Rapley, John W; Bray, Kimberly K; Cobb, Charles M

    2014-03-01

    Referral of periodontal patients requires development of a complex set of decision making skills. This study was conducted to determine criteria used by dental and dental hygiene students regarding the referral of periodontal patients for specialty care. Using mixed methods, a thirteen-item survey was developed to elicit the students' perceptions of their knowledge, confidence regarding managing patients, and clinical reasoning related to periodontal patients. The instrument was administered during the summer prior to (T1) and at the end of the students' final year (T2) of training. Seventy-nine dental students (81 percent of total class) and thirty dental hygiene students (83 percent of total class) completed T1. At T2, forty-two dental (44 percent of total class) and twenty-six dental hygiene students (87 percent of total class) completed the questionnaire. While 90 percent of dental and 96 percent of dental hygiene respondents reported a willingness to refer patients with active disease to specialists, only 40 percent of dental and 36 percent of dental hygiene respondents reported confidence in diagnosing, treating, and appropriately referring such patients. The students' ability to recognize critical disease and risk factors influencing referral was good; however, clinical application of that knowledge indicated a gap between knowledge and applied reasoning. The students' attitudes about the importance of periodontal disease and their perceived competence to identify critical disease risk factors were not significantly related (p>0.05) to correct clinical decisions in the case scenarios. The study concludes that dental and dental hygiene curricula should emphasize both the acquisition and application of knowledge regarding criteria for referral of periodontal patients.

  2. The NIAID Division of AIDS enterprise information system: integrated decision support for global clinical research programs.

    Science.gov (United States)

    Kagan, Jonathan M; Gupta, Nitin; Varghese, Suresh; Virkar, Hemant

    2011-12-01

    The National Institute of Allergy and Infectious Diseases (NIAID) Division of AIDS (DAIDS) Enterprise Information System (DAIDS-ES) is a web-based system that supports NIAID in the scientific, strategic, and tactical management of its global clinical research programs for HIV/AIDS vaccines, prevention, and therapeutics. Different from most commercial clinical trials information systems, which are typically protocol-driven, the DAIDS-ES was built to exchange information with those types of systems and integrate it in ways that help scientific program directors lead the research effort and keep pace with the complex and ever-changing global HIV/AIDS pandemic. Whereas commercially available clinical trials support systems are not usually disease-focused, DAIDS-ES was specifically designed to capture and incorporate unique scientific, demographic, and logistical aspects of HIV/AIDS treatment, prevention, and vaccine research in order to provide a rich source of information to guide informed decision-making. Sharing data across its internal components and with external systems, using defined vocabularies, open standards and flexible interfaces, the DAIDS-ES enables NIAID, its global collaborators and stakeholders, access to timely, quality information about NIAID-supported clinical trials which is utilized to: (1) analyze the research portfolio, assess capacity, identify opportunities, and avoid redundancies; (2) help support study safety, quality, ethics, and regulatory compliance; (3) conduct evidence-based policy analysis and business process re-engineering for improved efficiency. This report summarizes how the DAIDS-ES was conceptualized, how it differs from typical clinical trial support systems, the rationale for key design choices, and examples of how it is being used to advance the efficiency and effectiveness of NIAID's HIV/AIDS clinical research programs.

  3. Towards Automating Clinical Assessments: A Survey of the Timed Up and Go (TUG)

    OpenAIRE

    Sprint, Gina; Cook, Diane; Weeks, Douglas

    2015-01-01

    Older adults often suffer from functional impairments that affect their ability to perform everyday tasks. To detect the onset and changes in abilities, healthcare professionals administer standardized assessments. Recently, technology has been utilized to complement these clinical assessments to gain a more objective and detailed view of functionality. In the clinic and at home, technology is able to provide more information about patient performance and reduce subjectivity in outcome measur...

  4. Design and implementation of a decision support system for breast cancer treatment based on clinical practice guidelines

    International Nuclear Information System (INIS)

    Skevofilakas, M.T.; Nikita, K.S.; Templaleksis, P.H.; Birbas, K.N.; Kaklamanos, I.G.; Bonatsos, G.N.

    2007-01-01

    Evidence based medicine is the clinical practice that uses medical data and proof in order to make efficient clinical decisions. Information technology (IT) can play a crucial role in exploiting the huge size of raw medical data involved. In an attempt to improve clinical efficacy, health care society nowadays also utilizes a new assistant, clinical guidelines. Our research concerns the medical domain of the breast cancer disease. Our research's focus is twofold; our primary goal is to ensure consistency in clinical practice by importing clinical guidelines in an IT driven decision support system (DSS). Furthermore, we seek to improve visualization of disease specific, clinical data, providing for it's faster and more efficient use. (orig.)

  5. A clinical decision aid for the selection of antithrombotic therapy for the prevention of stroke due to atrial fibrillation

    DEFF Research Database (Denmark)

    LaHaye, Stephen Andrew; Gibbens, Sabra Lynn; Ball, David Gerald Andrew

    2012-01-01

    The availability of new antithrombotic agents, each with a unique efficacy and bleeding profile, has introduced a considerable amount of clinical uncertainty with physicians. We have developed a clinical decision aid in order to assist clinicians in determining an optimal antithrombotic regime...

  6. "Think aloud" and "Near live" usability testing of two complex clinical decision support tools.

    Science.gov (United States)

    Richardson, Safiya; Mishuris, Rebecca; O'Connell, Alexander; Feldstein, David; Hess, Rachel; Smith, Paul; McCullagh, Lauren; McGinn, Thomas; Mann, Devin

    2017-10-01

    Low provider adoption continues to be a significant barrier to realizing the potential of clinical decision support. "Think Aloud" and "Near Live" usability testing were conducted on two clinical decision support tools. Each was composed of an alert, a clinical prediction rule which estimated risk of either group A Streptococcus pharyngitis or pneumonia and an automatic order set based on risk. The objective of this study was to further understanding of the facilitators of usability and to evaluate the types of additional information gained from proceeding to "Near Live" testing after completing "Think Aloud". This was a qualitative observational study conducted at a large academic health care system with 12 primary care providers. During "Think Aloud" testing, participants were provided with written clinical scenarios and asked to verbalize their thought process while interacting with the tool. During "Near Live" testing participants interacted with a mock patient. Morae usability software was used to record full screen capture and audio during every session. Participant comments were placed into coding categories and analyzed for generalizable themes. Themes were compared across usability methods. "Think Aloud" and "Near Live" usability testing generated similar themes under the coding categories visibility, workflow, content, understand-ability and navigation. However, they generated significantly different themes under the coding categories usability, practical usefulness and medical usefulness. During both types of testing participants found the tool easier to use when important text was distinct in its appearance, alerts were passive and appropriately timed, content was up to date, language was clear and simple, and each component of the tool included obvious indicators of next steps. Participant comments reflected higher expectations for usability and usefulness during "Near Live" testing. For example, visit aids, such as automatically generated order sets

  7. [Which research is needed to support clinical decision-making on integrative medicine? Can comparative effectiveness research close the gap?].

    Science.gov (United States)

    Witt, Claudia M; Huang, Wen-jing; Lao, Lixing; Berman, Brian M

    2013-08-01

    In clinical research on complementary and integrative medicine, experts and scientists have often pursued a research agenda in spite of an incomplete understanding of the needs of end users. Consequently, the majority of previous clinical trials have mainly assessed the efficacy of interventions. Scant data is available on their effectiveness. Comparative effectiveness research (CER) promises to support decision makers by generating evidence that compares the benefits and harms of best care options. This evidence, more generalizable than evidence generated by traditional randomized clinical trials (RCTs), is better suited to inform real-world care decisions. An emphasis on CER supports the development of the evidence base for clinical and policy decision-making. Whereas in most areas of complementary and integrative medicine data on CER is scarce, available acupuncture research already contributes to CER evidence. This paper will introduce CER and make suggestions for future research.

  8. Physicians' perception of alternative displays of clinical research evidence for clinical decision support - A study with case vignettes.

    Science.gov (United States)

    Slager, Stacey L; Weir, Charlene R; Kim, Heejun; Mostafa, Javed; Del Fiol, Guilherme

    2017-07-01

    To design alternate information displays that present summaries of clinical trial results to clinicians to support decision-making; and to compare the displays according to efficacy and acceptability. A 6-between (information display presentation order) by 3-within (display type) factorial design. Two alternate displays were designed based on Information Foraging theory: a narrative summary that reduces the content to a few sentences; and a table format that structures the display according to the PICO (Population, Intervention, Comparison, Outcome) framework. The designs were compared with the summary display format available in PubMed. Physicians were asked to review five clinical studies retrieved for a case vignette; and were presented with the three display formats. Participants were asked to rate their experience with each of the information displays according to a Likert scale questionnaire. Twenty physicians completed the study. Overall, participants rated the table display more highly than either the text summary or PubMed's summary format (5.9vs. 5.4vs. 3.9 on a scale between 1 [strongly disagree] and 7 [strongly agree]). Usefulness ratings of seven pieces of information, i.e. patient population, patient age range, sample size, study arm, primary outcome, results of primary outcome, and conclusion, were high (average across all items=4.71 on a 1 to 5 scale, with 1=not at all useful and 5=very useful). Study arm, primary outcome, and conclusion scored the highest (4.9, 4.85, and 4.85 respectively). Participants suggested additional details such as rate of adverse effects. The table format reduced physicians' perceived cognitive effort when quickly reviewing clinical trial information and was more favorably received by physicians than the narrative summary or PubMed's summary format display. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Unintended adverse consequences of a clinical decision support system: two cases.

    Science.gov (United States)

    Stone, Erin G

    2017-09-23

    Many institutions have implemented clinical decision support systems (CDSSs). While CDSS research papers have focused on benefits of these systems, there is a smaller body of literature showing that CDSSs may also produce unintended adverse consequences (UACs). Detailed here are 2 cases of UACs resulting from a CDSS. Both of these cases were related to external systems that fed data into the CDSS. In the first case, lack of knowledge of data categorization in an external pharmacy system produced a UAC; in the second case, the change of a clinical laboratory instrument produced the UAC. CDSSs rely on data from many external systems. These systems are dynamic and may have changes in hardware, software, vendors, or processes. Such changes can affect the accuracy of CDSSs. These cases point to the need for the CDSS team to be familiar with these external systems. This team (manager and alert builders) should include members in specific clinical specialties with deep knowledge of these external systems. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  10. Using clinical decision support as a means of implementing a universal postpartum depression screening program.

    Science.gov (United States)

    Loudon, Holly; Nentin, Farida; Silverman, Michael E

    2016-06-01

    A major barrier to the diagnosis of postpartum depression (PPD) includes symptom detection. The lack of awareness and understanding of PPD among new mothers, the variability in clinical presentation, and the various diagnostic strategies can increase this further. The purpose of this study was to test the feasibility of adding clinical decision support (CDS) to the electronic health record (EHR) as a means of implementing a universal standardized PPD screening program within a large, at high risk, population. All women returning to the Mount Sinai Hospital OB/GYN Ambulatory Practice for postpartum care between 2010 and 2013 were presented with the Edinburgh Postnatal Depression Scale (EPDS) in response to a CDS "hard stop" built into the EHR. Of the 2102 women who presented for postpartum care, 2092 women (99.5 %) were screened for PPD in response to a CDS hard stop module. Screens were missing on ten records (0.5 %) secondary to refusal, language barrier, or lack of clarity in the EHR. Technology is becoming increasingly important in addressing the challenges faced by health care providers. While the identification of PPD has become the recent focus of public health concerns secondary to the significant social burden, numerous barriers to screening still exist within the clinical setting. The utility of adding CDS in the form of a hard stop, requiring clinicians to enter a standardized PPD mood assessment score to the patient EHR, offers a sufficient way to address a primary barrier to PPD symptom identification at the practitioner level.

  11. [The added value of information summaries supporting clinical decisions at the point-of-care.

    Science.gov (United States)

    Banzi, Rita; González-Lorenzo, Marien; Kwag, Koren Hyogene; Bonovas, Stefanos; Moja, Lorenzo

    2016-11-01

    Evidence-based healthcare requires the integration of the best research evidence with clinical expertise and patients' values. International publishers are developing evidence-based information services and resources designed to overcome the difficulties in retrieving, assessing and updating medical information as well as to facilitate a rapid access to valid clinical knowledge. Point-of-care information summaries are defined as web-based medical compendia that are specifically designed to deliver pre-digested, rapidly accessible, comprehensive, and periodically updated information to health care providers. Their validity must be assessed against marketing claims that they are evidence-based. We periodically evaluate the content development processes of several international point-of-care information summaries. The number of these products has increased along with their quality. The last analysis done in 2014 identified 26 products and found that three of them (Best Practice, Dynamed e Uptodate) scored the highest across all evaluated dimensions (volume, quality of the editorial process and evidence-based methodology). Point-of-care information summaries as stand-alone products or integrated with other systems, are gaining ground to support clinical decisions. The choice of one product over another depends both on the properties of the service and the preference of users. However, even the most innovative information system must rely on transparent and valid contents. Individuals and institutions should regularly assess the value of point-of-care summaries as their quality changes rapidly over time.

  12. Electronic clinical decision support systems attitudes and barriers to use in the oncology setting.

    LENUS (Irish Health Repository)

    Collins, I M

    2012-03-02

    BACKGROUND: There is little evidence regarding attitudes to clinical decision support systems (CDSS) in oncology. AIMS: We examined the current usage, awareness, and concerns of Irish medical oncologists and oncology pharmacists in this area. METHODS: A questionnaire was sent to 27 medical oncologists and 34 oncology pharmacists, identified through professional interest groups. Respondents ranked concerns regarding their use of a CDSS on a scale from 1 to 4, with 4 being most important. RESULTS: Overall, 67% (41\\/61) responded, 48% (13\\/27) of oncologists and 82% (28\\/34) of pharmacists surveyed. Concerns included "difficulty defining complex clinical situations with a set of rules" (mean ± SD) (3.2 ± 0.9), "ensuring evidence base is up to date and relevant" (3.2 ± 0.9) and "lack of clinically relevant suggestions" (2.9 ± 0.9). Ninety-three percent reported using a CDSS but 54% were unaware of this. CONCLUSION: While there are benefits to using a CDSS, concerns must be addressed through user education. This may be a starting point for a user-centred design approach to the development of future local systems through a consultative process.

  13. Clinical decision support system for early detection of prostate cancer from benign hyperplasia of prostate.

    Science.gov (United States)

    Ghaderzadeh, Mustafa

    2013-01-01

    There has been a growing research interest in the use of intelligent methods in medical informatics studies. Intelligent computer programs were implemented to aid physicians and other medical professionals in making difficult medical decisions. Prostate Neoplasia problems including benign hyperplasia and cancer of prostate are very common and cause significant delay in recovery and often require costly investigations before coming to its diagnosis. The conventional approach to build medical diagnostic system requires the formulation of rules by which the input data can be analyzed. But the formulation of such rules is very difficult with large sets of input data. Realizing the difficulty, a number of quantitative mathematical and statistical models including pattern classification technique such as Artificial neural networks (ANN), rolled based system, discriminate analysis and regression analysis has been applied as an alternative to conventional clinical and medical diagnostic. Among the mathematical and statistical modeling techniques used in medical decision support, Artificial neural networks attract many attentions in recent studies and in the last decade, the use of neural networks has become widely accepted in medical applications. This is manifested by an increasing number of medical devices currently available on the market with embedded AI algorithms, together with an accelerating pace of publication in medical journals, with over 500 academic publications year featuring Artificial Neural Networks (ANNs).

  14. Surgical and Clinical Decision Making in Isolated Long Thoracic Nerve Palsy.

    Science.gov (United States)

    Noland, Shelley S; Krauss, Emily M; Felder, John M; Mackinnon, Susan E

    2017-10-01

    Isolated long thoracic nerve palsy results in scapular winging and destabilization. In this study, we review the surgical management of isolated long thoracic nerve palsy and suggest a surgical technique and treatment algorithm to simplify management. In total, 19 patients who required surgery for an isolated long thoracic nerve palsy were reviewed retrospectively. Preoperative demographics, electromyography (EMG), and physical examinations were reviewed. Intraoperative nerve stimulation, surgical decision making, and postoperative outcomes were reviewed. In total, 19 patients with an average age of 32 were included in the study. All patients had an isolated long thoracic nerve palsy caused by either an injury (58%), Parsonage-Turner syndrome (32%), or shoulder surgery (10%); 18 patients (95%) underwent preoperative EMG; 10 with evidence of denervation (56%); and 13 patients had motor unit potentials in the serratus anterior (72%). The preoperative EMG did not correlate with intraoperative nerve stimulation in 13 patients (72%) and did correlate in 5 patients (28%); 3 patients had a nerve transfer (3 thoracodorsal to long thoracic at lateral chest, 1 pec to long thoracic at supraclavicular incision). In the 3 patients who had a nerve transfer, there was return of full forward flexion of the shoulder at an average of 2.5 months. A treatment algorithm based on intraoperative nerve stimulation will help guide surgeons in their clinical decision making in patients with isolated long thoracic nerve palsy. Intraoperative nerve stimulation is the gold standard in the management of isolated long thoracic nerve palsy.

  15. [From library to clinical decision support systems: access of general practitioner to quality information].

    Science.gov (United States)

    Fauquert, B

    2012-09-01

    Since 2003, the following tools have been implemented in Belgium for improving the access of general practioners to the EBM literature: the Digital Library for Health and the evidence-linker of the CEBAM, the portal EBMPracticeNet.be and the multidimensional electronic clinical decision support EBMeDS. The aim of this article is to show the progress achieved in the information dissemination toward the belgian general practioners, particularly the access from the electronic health record. From the literature published these last years, the opportunities cited by the users are for using EBM and the strong willingness for using these literature access in the future; the limits are the medical data coding, the irrelevance of the search results, the alerts fatigue induced by EBMeDS. The achievements done and planned for the new EBMPracticeNet guidelines portal and the EBMeDS system are explained in the aim of informing belgian healthcare professionals. These projects are claiming for lauching a participatory process in the production and dissemination of EBM information. The discussion is focused on the belgian healthcare system advantages, the solutions for a reasonable implementation of these projects and for increasing the place of an evidence-based information in the healthcare decision process. Finally the input of these projects to the continuing medical education and to the healthcare quality are discussed, in a context of multifactorial interaction healthcare design (complexity design).

  16. Adapting Cognitive Task Analysis to Investigate Clinical Decision Making and Medication Safety Incidents.

    Science.gov (United States)

    Russ, Alissa L; Militello, Laura G; Glassman, Peter A; Arthur, Karen J; Zillich, Alan J; Weiner, Michael

    2017-05-03

    Cognitive task analysis (CTA) can yield valuable insights into healthcare professionals' cognition and inform system design to promote safe, quality care. Our objective was to adapt CTA-the critical decision method, specifically-to investigate patient safety incidents, overcome barriers to implementing this method, and facilitate more widespread use of cognitive task analysis in healthcare. We adapted CTA to facilitate recruitment of healthcare professionals and developed a data collection tool to capture incidents as they occurred. We also leveraged the electronic health record (EHR) to expand data capture and used EHR-stimulated recall to aid reconstruction of safety incidents. We investigated 3 categories of medication-related incidents: adverse drug reactions, drug-drug interactions, and drug-disease interactions. Healthcare professionals submitted incidents, and a subset of incidents was selected for CTA. We analyzed several outcomes to characterize incident capture and completed CTA interviews. We captured 101 incidents. Eighty incidents (79%) met eligibility criteria. We completed 60 CTA interviews, 20 for each incident category. Capturing incidents before interviews allowed us to shorten the interview duration and reduced reliance on healthcare professionals' recall. Incorporating the EHR into CTA enriched data collection. The adapted CTA technique was successful in capturing specific categories of safety incidents. Our approach may be especially useful for investigating safety incidents that healthcare professionals "fix and forget." Our innovations to CTA are expected to expand the application of this method in healthcare and inform a wide range of studies on clinical decision making and patient safety.

  17. A global, incremental development method for a web-based prostate cancer treatment decision aid and usability testing in a Dutch clinical setting

    NARCIS (Netherlands)

    Cuypers, M.; Lamers, R.E.D.; Kil, P.J.M.; The, R.; Karssen, K.; van de Poll-Franse, L.V.; de Vries, M.

    2018-01-01

    Many new decision aids are developed while aspects of existing decision aids could also be useful, leading to a sub-optimal use of resources. To support treatment decision-making in prostate cancer patients, a pre-existing evidence-based Canadian decision aid was adjusted to Dutch clinical setting.

  18. Clinical Decision Support Tools for Selecting Interventions for Patients with Disabling Musculoskeletal Disorders

    DEFF Research Database (Denmark)

    Gross, Douglas P; Armijo-Olivo, Susan; Shaw, William S

    2016-01-01

    Purpose We aimed to identify and inventory clinical decision support (CDS) tools for helping front-line staff select interventions for patients with musculoskeletal (MSK) disorders. Methods We used Arksey and O'Malley's scoping review framework which progresses through five stages: (1) identifying...... multiple disciplines, we searched health care, computing science and business databases. Results Our search resulted in 4605 manuscripts. Titles and abstracts were screened for relevance. The reliability of the screening process was high with an average percentage of agreement of 92.3 %. Of the located...... rapidly advancing computer technologies, are under development and of potential interest to health care providers, case management organizations and funders of care. Based on the results of this scoping review, we conclude that these tools, models and systems should be subjected to further validation...

  19. Service Oriented Architecture for Clinical Decision Support: A Systematic Review and Future Directions

    Science.gov (United States)

    Loya, Salvador Rodriguez; Kawamoto, Kensaku; Chatwin, Chris; Huser, Vojtech

    2017-01-01

    The use of a service-oriented architecture (SOA) has been identified as a promising approach for improving health care by facilitating reliable clinical decision support (CDS). A review of the literature through October 2013 identified 44